Overview

Dataset statistics

Number of variables33
Number of observations10831
Missing cells111622
Missing cells (%)31.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.3 MiB
Average record size in memory7.1 KiB

Variable types

Text21
Numeric6
Categorical4
Boolean2

Alerts

cited_by is highly overall correlated with scopus_citations and 2 other fieldsHigh correlation
document_type is highly overall correlated with has_scopusHigh correlation
has_scopus is highly overall correlated with document_type and 2 other fieldsHigh correlation
has_wos is highly overall correlated with language_wos and 3 other fieldsHigh correlation
language is highly overall correlated with language_scopus and 1 other fieldsHigh correlation
language_scopus is highly overall correlated with has_scopus and 2 other fieldsHigh correlation
language_wos is highly overall correlated with has_wos and 2 other fieldsHigh correlation
scopus_citations is highly overall correlated with cited_by and 3 other fieldsHigh correlation
wos_citations_all is highly overall correlated with cited_by and 3 other fieldsHigh correlation
wos_citations_core is highly overall correlated with cited_by and 3 other fieldsHigh correlation
wos_reference_count is highly overall correlated with has_wosHigh correlation
document_type is highly imbalanced (58.5%)Imbalance
language is highly imbalanced (92.8%)Imbalance
has_scopus is highly imbalanced (64.6%)Imbalance
language_scopus is highly imbalanced (93.3%)Imbalance
language_wos is highly imbalanced (92.9%)Imbalance
journal has 2020 (18.7%) missing valuesMissing
publisher has 810 (7.5%) missing valuesMissing
scopus_citations has 724 (6.7%) missing valuesMissing
wos_citations_core has 6639 (61.3%) missing valuesMissing
wos_citations_all has 6639 (61.3%) missing valuesMissing
scopus_reference_count has 1001 (9.2%) missing valuesMissing
wos_reference_count has 6639 (61.3%) missing valuesMissing
wos_categories has 6654 (61.4%) missing valuesMissing
wos_research_areas has 6654 (61.4%) missing valuesMissing
authors has 754 (7.0%) missing valuesMissing
authors_wos has 6640 (61.3%) missing valuesMissing
author_full_names_wos has 6640 (61.3%) missing valuesMissing
affiliations_scopus has 872 (8.1%) missing valuesMissing
affiliations_wos has 6848 (63.2%) missing valuesMissing
addresses_wos has 6648 (61.4%) missing valuesMissing
author_keywords_scopus has 2589 (23.9%) missing valuesMissing
index_keywords_scopus has 4570 (42.2%) missing valuesMissing
author_keywords_wos has 6935 (64.0%) missing valuesMissing
keywords_plus_wos has 8288 (76.5%) missing valuesMissing
abstract_scopus has 724 (6.7%) missing valuesMissing
abstract_wos has 6720 (62.0%) missing valuesMissing
publisher_scopus has 1611 (14.9%) missing valuesMissing
publisher_wos has 6639 (61.3%) missing valuesMissing
language_scopus has 724 (6.7%) missing valuesMissing
language_wos has 6639 (61.3%) missing valuesMissing
cited_by is highly skewed (γ1 = 47.31713708)Skewed
scopus_citations is highly skewed (γ1 = 45.89522471)Skewed
wos_citations_core is highly skewed (γ1 = 36.20691352)Skewed
wos_citations_all is highly skewed (γ1 = 36.78078926)Skewed
cited_by has 2480 (22.9%) zerosZeros
scopus_citations has 2301 (21.2%) zerosZeros
wos_citations_core has 1047 (9.7%) zerosZeros
wos_citations_all has 903 (8.3%) zerosZeros

Reproduction

Analysis started2026-01-14 05:09:37.031921
Analysis finished2026-01-14 05:10:00.815546
Duration23.78 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

doi
Text

Distinct10830
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Memory size787.8 KiB
2026-01-14T05:10:01.237045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length66
Median length58
Mean length25.476547
Min length12

Characters and Unicode

Total characters275911
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10830 ?
Unique (%)100.0%

Sample

1st row10.1002/(sici)1096-9128(199601)8:1<47::aid-cpe194>3.0.co;2-9
2nd row10.1002/(sici)1097-0193(1999)8:2/3<128::aid-hbm10>3.0.co;2-g
3rd row10.1002/(sici)1097-0363(199706)24:12<1321::aid-fld562>3.0.co;2-l
4th row10.1002/(sici)1097-0363(199706)24:12<1353::aid-fld564>3.0.co;2-6
5th row10.1002/(sici)1097-0363(199706)24:12<1371::aid-fld565>3.0.co;2-7
ValueCountFrequency (%)
10.1002/9780470670606.wbecc00261
 
< 0.1%
10.1002/(sici)1096-9128(199601)8:1<47::aid-cpe194>3.0.co;2-91
 
< 0.1%
10.1002/(sici)1097-0193(1999)8:2/3<128::aid-hbm10>3.0.co;2-g1
 
< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1321::aid-fld562>3.0.co;2-l1
 
< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1353::aid-fld564>3.0.co;2-61
 
< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1371::aid-fld565>3.0.co;2-71
 
< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1417::aid-fld567>3.0.co;2-n1
 
< 0.1%
10.1002/(sici)1097-0363(199706)24:12<1449::aid-fld569>3.0.co;2-81
 
< 0.1%
10.1002/(sici)1098-111x(199702)12:2<105::aid-int1>3.0.co;2-u1
 
< 0.1%
10.1002/(sici)1098-111x(199911)14:11<1071::aid-int1>3.0.co;2-j1
 
< 0.1%
Other values (10820)10820
99.9%
2026-01-14T05:10:02.351264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
143930
15.9%
042913
15.6%
.24004
 
8.7%
220402
 
7.4%
316725
 
6.1%
914171
 
5.1%
712822
 
4.6%
412372
 
4.5%
512348
 
4.5%
811762
 
4.3%
Other values (38)64462
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)275911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
143930
15.9%
042913
15.6%
.24004
 
8.7%
220402
 
7.4%
316725
 
6.1%
914171
 
5.1%
712822
 
4.6%
412372
 
4.5%
512348
 
4.5%
811762
 
4.3%
Other values (38)64462
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)275911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
143930
15.9%
042913
15.6%
.24004
 
8.7%
220402
 
7.4%
316725
 
6.1%
914171
 
5.1%
712822
 
4.6%
412372
 
4.5%
512348
 
4.5%
811762
 
4.3%
Other values (38)64462
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)275911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
143930
15.9%
042913
15.6%
.24004
 
8.7%
220402
 
7.4%
316725
 
6.1%
914171
 
5.1%
712822
 
4.6%
412372
 
4.5%
512348
 
4.5%
811762
 
4.3%
Other values (38)64462
23.4%

title
Text

Distinct10776
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2026-01-14T05:10:03.033865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length476
Median length259
Mean length93.42009
Min length6

Characters and Unicode

Total characters1011833
Distinct characters700
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10731 ?
Unique (%)99.1%

Sample

1st rowBenchmarking the computation and communication performance of the CM-5
2nd rowComputational modeling of high-level cognition and brain function
3rd rowParallel computation of incompressible flows with complex geometries
4th rowParallel finite element simulation of large ram-air parachutes
5th rowParallel finite element methods for large-scale computation of storm surges and tidal flows
ValueCountFrequency (%)
of5376
 
4.1%
and4854
 
3.7%
in4486
 
3.4%
computational4310
 
3.3%
thinking4264
 
3.3%
the3745
 
2.9%
a3593
 
2.8%
for2646
 
2.0%
to1967
 
1.5%
learning1743
 
1.3%
Other values (13021)93466
71.6%
2026-01-14T05:10:03.784178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
119386
 
11.8%
i80198
 
7.9%
n79272
 
7.8%
e76564
 
7.6%
t67002
 
6.6%
a66775
 
6.6%
o66645
 
6.6%
r46219
 
4.6%
s41410
 
4.1%
l34944
 
3.5%
Other values (690)333418
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1011833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
119386
 
11.8%
i80198
 
7.9%
n79272
 
7.8%
e76564
 
7.6%
t67002
 
6.6%
a66775
 
6.6%
o66645
 
6.6%
r46219
 
4.6%
s41410
 
4.1%
l34944
 
3.5%
Other values (690)333418
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1011833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
119386
 
11.8%
i80198
 
7.9%
n79272
 
7.8%
e76564
 
7.6%
t67002
 
6.6%
a66775
 
6.6%
o66645
 
6.6%
r46219
 
4.6%
s41410
 
4.1%
l34944
 
3.5%
Other values (690)333418
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1011833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
119386
 
11.8%
i80198
 
7.9%
n79272
 
7.8%
e76564
 
7.6%
t67002
 
6.6%
a66775
 
6.6%
o66645
 
6.6%
r46219
 
4.6%
s41410
 
4.1%
l34944
 
3.5%
Other values (690)333418
33.0%

year
Real number (ℝ)

Distinct48
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.465
Minimum1970
Maximum2026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.3 KiB
2026-01-14T05:10:03.908752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile2008
Q12018
median2021
Q32024
95-th percentile2025
Maximum2026
Range56
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.025465
Coefficient of variation (CV)0.0029836938
Kurtosis6.6109073
Mean2019.465
Median Absolute Deviation (MAD)3
Skewness-2.1895759
Sum21872825
Variance36.306228
MonotonicityNot monotonic
2026-01-14T05:10:04.043172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
20241377
12.7%
20251366
12.6%
20231188
11.0%
20221070
9.9%
2021982
9.1%
2020852
7.9%
2019723
 
6.7%
2018562
 
5.2%
2017467
 
4.3%
2016302
 
2.8%
Other values (38)1942
17.9%
ValueCountFrequency (%)
19701
 
< 0.1%
19751
 
< 0.1%
19801
 
< 0.1%
19811
 
< 0.1%
19821
 
< 0.1%
19834
< 0.1%
19846
0.1%
19863
< 0.1%
19873
< 0.1%
19887
0.1%
ValueCountFrequency (%)
202664
 
0.6%
20251366
12.6%
20241377
12.7%
20231188
11.0%
20221070
9.9%
2021982
9.1%
2020852
7.9%
2019723
6.7%
2018562
5.2%
2017467
 
4.3%

journal
Text

Missing 

Distinct2639
Distinct (%)30.0%
Missing2020
Missing (%)18.7%
Memory size856.7 KiB
2026-01-14T05:10:04.363872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length242
Median length115
Mean length43.213256
Min length3

Characters and Unicode

Total characters380752
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1666 ?
Unique (%)18.9%

Sample

1st rowConcurrency Practice and Experience
2nd rowHuman Brain Mapping
3rd rowInternational Journal for Numerical Methods in Fluids
4th rowInternational Journal for Numerical Methods in Fluids
5th rowInternational Journal for Numerical Methods in Fluids
ValueCountFrequency (%)
of2732
 
5.6%
and2708
 
5.6%
education2457
 
5.1%
in2253
 
4.6%
journal1784
 
3.7%
conference1673
 
3.4%
science1520
 
3.1%
international1488
 
3.1%
on1374
 
2.8%
computer1253
 
2.6%
Other values (2267)29387
60.4%
2026-01-14T05:10:04.855707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39818
 
10.5%
n29564
 
7.8%
e26567
 
7.0%
o21919
 
5.8%
i20804
 
5.5%
a17775
 
4.7%
t15316
 
4.0%
c14553
 
3.8%
E14486
 
3.8%
r14419
 
3.8%
Other values (67)165531
43.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)380752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
39818
 
10.5%
n29564
 
7.8%
e26567
 
7.0%
o21919
 
5.8%
i20804
 
5.5%
a17775
 
4.7%
t15316
 
4.0%
c14553
 
3.8%
E14486
 
3.8%
r14419
 
3.8%
Other values (67)165531
43.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)380752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
39818
 
10.5%
n29564
 
7.8%
e26567
 
7.0%
o21919
 
5.8%
i20804
 
5.5%
a17775
 
4.7%
t15316
 
4.0%
c14553
 
3.8%
E14486
 
3.8%
r14419
 
3.8%
Other values (67)165531
43.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)380752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
39818
 
10.5%
n29564
 
7.8%
e26567
 
7.0%
o21919
 
5.8%
i20804
 
5.5%
a17775
 
4.7%
t15316
 
4.0%
c14553
 
3.8%
E14486
 
3.8%
r14419
 
3.8%
Other values (67)165531
43.5%

document_type
Categorical

High correlation  Imbalance 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size634.1 KiB
Article
4874 
Conference paper
4073 
Book chapter
792 
Review
 
468
Book
 
188
Other values (20)
 
436

Length

Max length32
Median length26
Mean length10.938233
Min length4

Characters and Unicode

Total characters118472
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowArticle
2nd rowConference paper
3rd rowArticle
4th rowArticle
5th rowArticle

Common Values

ValueCountFrequency (%)
Article4874
45.0%
Conference paper4073
37.6%
Book chapter792
 
7.3%
Review468
 
4.3%
Book188
 
1.7%
Proceedings Paper174
 
1.6%
Editorial49
 
0.5%
Note46
 
0.4%
Article; Book Chapter46
 
0.4%
Erratum28
 
0.3%
Other values (15)93
 
0.9%

Length

2026-01-14T05:10:04.985233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
article4940
30.8%
paper4251
26.5%
conference4073
25.4%
book1032
 
6.4%
chapter840
 
5.2%
review477
 
3.0%
proceedings176
 
1.1%
editorial60
 
0.4%
note46
 
0.3%
erratum28
 
0.2%
Other values (12)139
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e23719
20.0%
r14499
12.2%
c10046
8.5%
p9167
 
7.7%
n8335
 
7.0%
o6438
 
5.4%
t6023
 
5.1%
i5737
 
4.8%
a5249
 
4.4%
5231
 
4.4%
Other values (25)24028
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)118472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e23719
20.0%
r14499
12.2%
c10046
8.5%
p9167
 
7.7%
n8335
 
7.0%
o6438
 
5.4%
t6023
 
5.1%
i5737
 
4.8%
a5249
 
4.4%
5231
 
4.4%
Other values (25)24028
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)118472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e23719
20.0%
r14499
12.2%
c10046
8.5%
p9167
 
7.7%
n8335
 
7.0%
o6438
 
5.4%
t6023
 
5.1%
i5737
 
4.8%
a5249
 
4.4%
5231
 
4.4%
Other values (25)24028
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)118472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e23719
20.0%
r14499
12.2%
c10046
8.5%
p9167
 
7.7%
n8335
 
7.0%
o6438
 
5.4%
t6023
 
5.1%
i5737
 
4.8%
a5249
 
4.4%
5231
 
4.4%
Other values (25)24028
20.3%

publisher
Text

Missing 

Distinct1029
Distinct (%)10.3%
Missing810
Missing (%)7.5%
Memory size833.1 KiB
2026-01-14T05:10:05.233016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length130
Median length104
Mean length33.496757
Min length3

Characters and Unicode

Total characters335671
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique556 ?
Unique (%)5.5%

Sample

1st rowJohn Wiley and Sons Ltd
2nd rowJohn Wiley and Sons Ltd
3rd rowJohn Wiley and Sons Ltd
4th rowJohn Wiley and Sons Ltd
5th rowJohn Wiley and Sons Ltd
ValueCountFrequency (%)
and2674
 
6.3%
inc2178
 
5.1%
of1892
 
4.5%
springer1878
 
4.4%
institute1586
 
3.7%
association1424
 
3.4%
for1400
 
3.3%
computing1304
 
3.1%
machinery1301
 
3.1%
engineers1210
 
2.9%
Other values (1584)25493
60.2%
2026-01-14T05:10:05.663071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32319
 
9.6%
i26513
 
7.9%
n25574
 
7.6%
e24990
 
7.4%
r17882
 
5.3%
c17833
 
5.3%
t16787
 
5.0%
s16776
 
5.0%
a16289
 
4.9%
o15693
 
4.7%
Other values (70)125015
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)335671
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32319
 
9.6%
i26513
 
7.9%
n25574
 
7.6%
e24990
 
7.4%
r17882
 
5.3%
c17833
 
5.3%
t16787
 
5.0%
s16776
 
5.0%
a16289
 
4.9%
o15693
 
4.7%
Other values (70)125015
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)335671
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32319
 
9.6%
i26513
 
7.9%
n25574
 
7.6%
e24990
 
7.4%
r17882
 
5.3%
c17833
 
5.3%
t16787
 
5.0%
s16776
 
5.0%
a16289
 
4.9%
o15693
 
4.7%
Other values (70)125015
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)335671
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32319
 
9.6%
i26513
 
7.9%
n25574
 
7.6%
e24990
 
7.4%
r17882
 
5.3%
c17833
 
5.3%
t16787
 
5.0%
s16776
 
5.0%
a16289
 
4.9%
o15693
 
4.7%
Other values (70)125015
37.2%

language
Categorical

High correlation  Imbalance 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size592.6 KiB
English
10464 
Spanish
 
162
Chinese
 
84
Portuguese
 
61
Russian
 
19
Other values (12)
 
41

Length

Max length10
Median length7
Mean length7.015511
Min length6

Characters and Unicode

Total characters75985
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English10464
96.6%
Spanish162
 
1.5%
Chinese84
 
0.8%
Portuguese61
 
0.6%
Russian19
 
0.2%
German8
 
0.1%
French6
 
0.1%
Korean4
 
< 0.1%
Polish4
 
< 0.1%
Italian4
 
< 0.1%
Other values (7)15
 
0.1%

Length

2026-01-14T05:10:05.806075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english10464
96.6%
spanish162
 
1.5%
chinese84
 
0.8%
portuguese61
 
0.6%
russian19
 
0.2%
german8
 
0.1%
french6
 
0.1%
korean4
 
< 0.1%
polish4
 
< 0.1%
italian4
 
< 0.1%
Other values (7)15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
s10820
14.2%
n10762
14.2%
i10750
14.1%
h10723
14.1%
g10526
13.9%
l10472
13.8%
E10464
13.8%
e315
 
0.4%
a221
 
0.3%
p165
 
0.2%
Other values (21)767
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)75985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s10820
14.2%
n10762
14.2%
i10750
14.1%
h10723
14.1%
g10526
13.9%
l10472
13.8%
E10464
13.8%
e315
 
0.4%
a221
 
0.3%
p165
 
0.2%
Other values (21)767
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)75985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s10820
14.2%
n10762
14.2%
i10750
14.1%
h10723
14.1%
g10526
13.9%
l10472
13.8%
E10464
13.8%
e315
 
0.4%
a221
 
0.3%
p165
 
0.2%
Other values (21)767
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)75985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s10820
14.2%
n10762
14.2%
i10750
14.1%
h10723
14.1%
g10526
13.9%
l10472
13.8%
E10464
13.8%
e315
 
0.4%
a221
 
0.3%
p165
 
0.2%
Other values (21)767
 
1.0%

cited_by
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct308
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.698366
Minimum0
Maximum10043
Zeros2480
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size84.7 KiB
2026-01-14T05:10:05.927580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q314
95-th percentile75
Maximum10043
Range10043
Interquartile range (IQR)13

Descriptive statistics

Standard deviation135.71259
Coefficient of variation (CV)6.5566814
Kurtosis3062.8841
Mean20.698366
Median Absolute Deviation (MAD)4
Skewness47.317137
Sum224184
Variance18417.907
MonotonicityNot monotonic
2026-01-14T05:10:06.067078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02480
22.9%
11315
 
12.1%
2834
 
7.7%
3659
 
6.1%
4534
 
4.9%
5411
 
3.8%
6333
 
3.1%
7293
 
2.7%
8264
 
2.4%
9243
 
2.2%
Other values (298)3465
32.0%
ValueCountFrequency (%)
02480
22.9%
11315
12.1%
2834
 
7.7%
3659
 
6.1%
4534
 
4.9%
5411
 
3.8%
6333
 
3.1%
7293
 
2.7%
8264
 
2.4%
9243
 
2.2%
ValueCountFrequency (%)
100431
< 0.1%
55091
< 0.1%
31611
< 0.1%
31561
< 0.1%
21231
< 0.1%
19081
< 0.1%
17031
< 0.1%
14381
< 0.1%
13171
< 0.1%
13061
< 0.1%

scopus_citations
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct306
Distinct (%)3.0%
Missing724
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean21.363411
Minimum0
Maximum10043
Zeros2301
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size84.7 KiB
2026-01-14T05:10:06.203853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q314
95-th percentile77.7
Maximum10043
Range10043
Interquartile range (IQR)13

Descriptive statistics

Standard deviation140.28213
Coefficient of variation (CV)6.5664665
Kurtosis2874.1411
Mean21.363411
Median Absolute Deviation (MAD)4
Skewness45.895225
Sum215920
Variance19679.075
MonotonicityNot monotonic
2026-01-14T05:10:06.340804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02301
21.2%
11201
 
11.1%
2793
 
7.3%
3613
 
5.7%
4502
 
4.6%
5379
 
3.5%
6308
 
2.8%
7264
 
2.4%
8249
 
2.3%
9226
 
2.1%
Other values (296)3271
30.2%
(Missing)724
 
6.7%
ValueCountFrequency (%)
02301
21.2%
11201
11.1%
2793
 
7.3%
3613
 
5.7%
4502
 
4.6%
5379
 
3.5%
6308
 
2.8%
7264
 
2.4%
8249
 
2.3%
9226
 
2.1%
ValueCountFrequency (%)
100431
< 0.1%
55091
< 0.1%
31611
< 0.1%
31561
< 0.1%
21231
< 0.1%
19081
< 0.1%
17031
< 0.1%
14381
< 0.1%
13171
< 0.1%
13061
< 0.1%

wos_citations_core
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct156
Distinct (%)3.7%
Missing6639
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean13.628101
Minimum0
Maximum3737
Zeros1047
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size84.7 KiB
2026-01-14T05:10:06.479227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q311
95-th percentile50
Maximum3737
Range3737
Interquartile range (IQR)10

Descriptive statistics

Standard deviation72.140259
Coefficient of variation (CV)5.2934931
Kurtosis1738.7809
Mean13.628101
Median Absolute Deviation (MAD)3
Skewness36.206914
Sum57129
Variance5204.2169
MonotonicityNot monotonic
2026-01-14T05:10:06.617223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01047
 
9.7%
1516
 
4.8%
2358
 
3.3%
3267
 
2.5%
4189
 
1.7%
5188
 
1.7%
6152
 
1.4%
7134
 
1.2%
896
 
0.9%
995
 
0.9%
Other values (146)1150
 
10.6%
(Missing)6639
61.3%
ValueCountFrequency (%)
01047
9.7%
1516
4.8%
2358
 
3.3%
3267
 
2.5%
4189
 
1.7%
5188
 
1.7%
6152
 
1.4%
7134
 
1.2%
896
 
0.9%
995
 
0.9%
ValueCountFrequency (%)
37371
< 0.1%
13371
< 0.1%
9691
< 0.1%
8161
< 0.1%
7791
< 0.1%
7521
< 0.1%
5451
< 0.1%
4841
< 0.1%
4551
< 0.1%
3841
< 0.1%

wos_citations_all
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct184
Distinct (%)4.4%
Missing6639
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean17.158874
Minimum0
Maximum4918
Zeros903
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size84.7 KiB
2026-01-14T05:10:06.762416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q313
95-th percentile62
Maximum4918
Range4918
Interquartile range (IQR)12

Descriptive statistics

Standard deviation94.431209
Coefficient of variation (CV)5.5033453
Kurtosis1777.9325
Mean17.158874
Median Absolute Deviation (MAD)4
Skewness36.780789
Sum71930
Variance8917.2532
MonotonicityNot monotonic
2026-01-14T05:10:06.912529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0903
 
8.3%
1510
 
4.7%
2348
 
3.2%
3238
 
2.2%
4204
 
1.9%
5164
 
1.5%
6150
 
1.4%
7138
 
1.3%
9100
 
0.9%
8100
 
0.9%
Other values (174)1337
 
12.3%
(Missing)6639
61.3%
ValueCountFrequency (%)
0903
8.3%
1510
4.7%
2348
 
3.2%
3238
 
2.2%
4204
 
1.9%
5164
 
1.5%
6150
 
1.4%
7138
 
1.3%
8100
 
0.9%
9100
 
0.9%
ValueCountFrequency (%)
49181
< 0.1%
17661
< 0.1%
13141
< 0.1%
10811
< 0.1%
9941
< 0.1%
9181
< 0.1%
7281
< 0.1%
6011
< 0.1%
5451
< 0.1%
4721
< 0.1%
Distinct9792
Distinct (%)99.6%
Missing1001
Missing (%)9.2%
Memory size18.5 MiB
2026-01-14T05:10:07.366642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2254
Median length1423
Mean length1063.7052
Min length9

Characters and Unicode

Total characters10456222
Distinct characters260
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9759 ?
Unique (%)99.3%

Sample

1st rowSolving Problems on Concurrent Processors, (1988); Hypercube Algorithms with Applications to Image Processing and Pattern Recognition, (1990); Bomans, Luc, Benchmarking the iPSC/2 hypercube multiprocessor, Concurrency Practice and Experience, 1, 1, pp. 3-18, (1989); Proceedings of the Frontiers of Massively Parallel Computation, (1992); Hockney, Roger W., Performance parameters and benchmarking of supercomputers, Parallel Computing, 17, 10-11, pp. 1111-1130, (1991); Kwan, Thomas T., Communication and computation performance of the CM-5, pp. 192-201, (1993); Leiserson, Charles E., Network architecture of the Connection Machine CM-5, pp. 272-285, (1992); Lin, Mengjou, Performance evaluation of the CM-5 interconnection network, pp. 189-198, (1993); Ponnusamy, Ravi, Experimental performance evaluation of the CM-5, Journal of Parallel and Distributed Computing, 19, 3, pp. 192-202, (1993); Bailey, David H., The nas parallel benchmarks, International Journal of High Performance Computing Applications, 5, 3, pp. 63-73, (1991)
2nd rowRules of the Mind, (1993); Awh, Edward, Dissociation of Storage and Rehearsal in Verbal Working Memory: Evidence from Positron Emission Tomography, Psychological Science, 7, 1, pp. 25-31, (1996); Agrammatism, (1985); Carpenter, Patricia Ann, Graded functional activation in the visuospatial system with the amount of task demand, Journal of Cognitive Neuroscience, 11, 1, pp. 9-24, (1999); Carpenter, Patricia Ann, What one intelligence test measures: A theoretical account of the processing in the Raven progressive matrices test, Psychological Review, 97, 3, pp. 404-431, (1990); Collins, Allan M., Retrieval time from semantic memory, Journal of Verbal Learning and Verbal Behavior, 8, 2, pp. 240-247, (1969); Rethinking Innateness A Connectionist Perspective on Development, (1996); Human Brain Function, (1997); Gabrieli, John D.E., The role of left prefrontal cortex in language and memory, Proceedings of the National Academy of Sciences of the United States of America, 95, 3, pp. 906-913, (1998); Grafman, Jordan Henry, Similarities and Distinctions among Current Models of Prefrontal Cortical Functions, Annals of the New York Academy of Sciences, 769, 1, pp. 337-368, (1995)
3rd rowTezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Añón, J. C R, Computation of incompressible flows with implicit finite element implementations on the Connection Machine, Computer Methods in Applied Mechanics and Engineering, 108, 1-2, pp. 99-118, (1993); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Computational Mechanics 95 Proc Int Conf on Computational Engineering Science, (1995); Pvm Parallel Virtual Machine, (1994); Añón, J. C R, An efficient communications strategy for finite element methods on the Connection Machine CM-5 system, Computer Methods in Applied Mechanics and Engineering, 113, 3-4, pp. 363-387, (1994); Mesh Generation and Update Strategies for Parallel Computation of Flow Problems with Moving Boundaries and Interfaces, (1995); Technical Report, (1995); Aliabadi, Shabrouz K., Parallel fluid dynamics computations in aerospace applications, International Journal for Numerical Methods in Fluids, 21, 10, pp. 783-805, (1995); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994)
4th rowAñón, J. C R, Development testing of large ram air inflated wings, (1993); Garrard, William L., Inflation analysis of ram air inflated gliding parachutes, pp. 186-198, (1995); Aliabadi, Shahrouz K., Parallel finite element computation of the dynamics of large ram air parachutes, pp. 278-293, (1995); Añón, J. C R, SEMI-EMPIRICAL THEORY TO PREDICT THE LOAD-TIME HISTORY OF AN INFLATING PARACHUTE., pp. 177-185, (1984); University of Minnesota Parachute Systems Technology Short Courses, (1994); AIAA Paper 91 0862, (1991); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: I. The concept and the preliminary numerical tests, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 339-351, (1992); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: II. Computation of free-surface flows, two-liquid flows, and flows with drifting cylinders, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 353-371, (1992)
5th rowAñón, J. C R, Tide and storm surge predictions using finite element model, Journal of Hydraulic Engineering, 118, 10, pp. 1373-1390, (1992); Añón, J. C R, Massively parallel finite element method for large scale computation of storm surge, 2, pp. 79-86, (1996); Añón, J. C R, Three‐step explicit finite element computation of shallow water flows on a massively parallel computer, International Journal for Numerical Methods in Fluids, 21, 10, pp. 885-900, (1995); Añón, J. C R, Selective lumping finite element method for shallow water flow, International Journal for Numerical Methods in Fluids, 2, 1, pp. 89-112, (1982); Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Hughes, Thomas J.R., A multi-dimensioal upwind scheme with no crosswind diffusion., 34 )., pp. 19-35, (1979); Tezduyar, Tayfun E., FINITE ELEMENT FORMULATIONS FOR CONVECTION DOMINATED FLOWS WITH PARTICULAR EMPHASIS ON THE COMPRESSIBLE EULER EQUATIONS., (1983); Añón, J. C R, Finite element computation of compressible flows with the SUPG formulation, American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM, 123, pp. 21-27, (1991); Añón, J. C R, SUPG finite element computation of compressible flows with the entropy and conservation variables formulations, Computer Methods in Applied Mechanics and Engineering, 104, 3, pp. 397-422, (1993); Aliabadi, Shabrouz K., SUPG finite element computation of viscous compressible flows based on the conservation and entropy variables formulations, Computational Mechanics, 11, 5-6, pp. 300-312, (1993)
ValueCountFrequency (%)
of54578
 
3.9%
and46033
 
3.3%
pp44781
 
3.2%
the32471
 
2.3%
in31804
 
2.3%
a21555
 
1.5%
education19887
 
1.4%
for16294
 
1.2%
thinking14857
 
1.1%
computational14605
 
1.0%
Other values (83956)1109855
78.9%
2026-01-14T05:10:07.989260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1396716
 
13.4%
e699321
 
6.7%
n680439
 
6.5%
i625728
 
6.0%
a586073
 
5.6%
o568802
 
5.4%
t499713
 
4.8%
r419513
 
4.0%
,374416
 
3.6%
s342841
 
3.3%
Other values (250)4262660
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10456222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1396716
 
13.4%
e699321
 
6.7%
n680439
 
6.5%
i625728
 
6.0%
a586073
 
5.6%
o568802
 
5.4%
t499713
 
4.8%
r419513
 
4.0%
,374416
 
3.6%
s342841
 
3.3%
Other values (250)4262660
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10456222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1396716
 
13.4%
e699321
 
6.7%
n680439
 
6.5%
i625728
 
6.0%
a586073
 
5.6%
o568802
 
5.4%
t499713
 
4.8%
r419513
 
4.0%
,374416
 
3.6%
s342841
 
3.3%
Other values (250)4262660
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10456222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1396716
 
13.4%
e699321
 
6.7%
n680439
 
6.5%
i625728
 
6.0%
a586073
 
5.6%
o568802
 
5.4%
t499713
 
4.8%
r419513
 
4.0%
,374416
 
3.6%
s342841
 
3.3%
Other values (250)4262660
40.8%

wos_reference_count
Real number (ℝ)

High correlation  Missing 

Distinct179
Distinct (%)4.3%
Missing6639
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean46.835639
Minimum0
Maximum678
Zeros62
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size84.7 KiB
2026-01-14T05:10:08.123229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q122
median41.5
Q364
95-th percentile104
Maximum678
Range678
Interquartile range (IQR)42

Descriptive statistics

Standard deviation33.769355
Coefficient of variation (CV)0.72101833
Kurtosis30.96061
Mean46.835639
Median Absolute Deviation (MAD)20.5
Skewness2.5960496
Sum196335
Variance1140.3693
MonotonicityNot monotonic
2026-01-14T05:10:08.266317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3372
 
0.7%
3170
 
0.6%
1564
 
0.6%
3963
 
0.6%
2162
 
0.6%
062
 
0.6%
2960
 
0.6%
2859
 
0.5%
1857
 
0.5%
5857
 
0.5%
Other values (169)3566
32.9%
(Missing)6639
61.3%
ValueCountFrequency (%)
062
0.6%
122
 
0.2%
212
 
0.1%
334
0.3%
430
0.3%
545
0.4%
645
0.4%
747
0.4%
844
0.4%
936
0.3%
ValueCountFrequency (%)
6781
< 0.1%
3101
< 0.1%
2171
< 0.1%
2082
< 0.1%
2071
< 0.1%
2031
< 0.1%
2011
< 0.1%
1911
< 0.1%
1901
< 0.1%
1891
< 0.1%

wos_categories
Text

Missing 

Distinct396
Distinct (%)9.5%
Missing6654
Missing (%)61.4%
Memory size661.8 KiB
2026-01-14T05:10:08.465369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length258
Median length187
Mean length62.231506
Min length3

Characters and Unicode

Total characters259941
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)4.6%

Sample

1st rowPsychology, Experimental
2nd rowPsychology, Experimental
3rd rowBiochemistry & Molecular Biology; Education, Scientific Disciplines
4th rowComputer Science, Interdisciplinary Applications; Education, Scientific Disciplines; Engineering, Multidisciplinary
5th rowComputer Science, Interdisciplinary Applications; Education, Scientific Disciplines; Engineering, Multidisciplinary
ValueCountFrequency (%)
education3403
12.6%
3293
12.2%
science2772
10.2%
computer2611
9.7%
educational2285
 
8.4%
research2246
 
8.3%
scientific1155
 
4.3%
disciplines1155
 
4.3%
interdisciplinary976
 
3.6%
applications928
 
3.4%
Other values (142)6227
23.0%
2026-01-14T05:10:08.812483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i26571
 
10.2%
22874
 
8.8%
e22226
 
8.6%
c21990
 
8.5%
n18806
 
7.2%
t15396
 
5.9%
a14629
 
5.6%
o13188
 
5.1%
r10659
 
4.1%
s9521
 
3.7%
Other values (37)84081
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)259941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i26571
 
10.2%
22874
 
8.8%
e22226
 
8.6%
c21990
 
8.5%
n18806
 
7.2%
t15396
 
5.9%
a14629
 
5.6%
o13188
 
5.1%
r10659
 
4.1%
s9521
 
3.7%
Other values (37)84081
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)259941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i26571
 
10.2%
22874
 
8.8%
e22226
 
8.6%
c21990
 
8.5%
n18806
 
7.2%
t15396
 
5.9%
a14629
 
5.6%
o13188
 
5.1%
r10659
 
4.1%
s9521
 
3.7%
Other values (37)84081
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)259941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i26571
 
10.2%
22874
 
8.8%
e22226
 
8.6%
c21990
 
8.5%
n18806
 
7.2%
t15396
 
5.9%
a14629
 
5.6%
o13188
 
5.1%
r10659
 
4.1%
s9521
 
3.7%
Other values (37)84081
32.3%

wos_research_areas
Text

Missing 

Distinct187
Distinct (%)4.5%
Missing6654
Missing (%)61.4%
Memory size554.3 KiB
2026-01-14T05:10:09.074165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length140
Median length139
Mean length35.889155
Min length3

Characters and Unicode

Total characters149909
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)2.6%

Sample

1st rowPsychology
2nd rowPsychology
3rd rowBiochemistry & Molecular Biology; Education & Educational Research
4th rowComputer Science; Education & Educational Research; Engineering
5th rowComputer Science; Education & Educational Research; Engineering
ValueCountFrequency (%)
3431
18.8%
research2966
16.3%
education2961
16.3%
educational2961
16.3%
science1916
10.5%
computer1721
9.4%
engineering426
 
2.3%
psychology185
 
1.0%
other157
 
0.9%
topics157
 
0.9%
Other values (109)1339
 
7.3%
2026-01-14T05:10:09.503354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c14075
 
9.4%
14043
 
9.4%
e13494
 
9.0%
a12524
 
8.4%
n10005
 
6.7%
i9999
 
6.7%
o9433
 
6.3%
t8545
 
5.7%
u7975
 
5.3%
E6486
 
4.3%
Other values (35)43330
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)149909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c14075
 
9.4%
14043
 
9.4%
e13494
 
9.0%
a12524
 
8.4%
n10005
 
6.7%
i9999
 
6.7%
o9433
 
6.3%
t8545
 
5.7%
u7975
 
5.3%
E6486
 
4.3%
Other values (35)43330
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)149909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c14075
 
9.4%
14043
 
9.4%
e13494
 
9.0%
a12524
 
8.4%
n10005
 
6.7%
i9999
 
6.7%
o9433
 
6.3%
t8545
 
5.7%
u7975
 
5.3%
E6486
 
4.3%
Other values (35)43330
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)149909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c14075
 
9.4%
14043
 
9.4%
e13494
 
9.0%
a12524
 
8.4%
n10005
 
6.7%
i9999
 
6.7%
o9433
 
6.3%
t8545
 
5.7%
u7975
 
5.3%
E6486
 
4.3%
Other values (35)43330
28.9%

has_scopus
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
True
10107 
False
 
724
ValueCountFrequency (%)
True10107
93.3%
False724
 
6.7%
2026-01-14T05:10:09.581827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

has_wos
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
False
6639 
True
4192 
ValueCountFrequency (%)
False6639
61.3%
True4192
38.7%
2026-01-14T05:10:09.630247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

authors
Text

Missing 

Distinct9360
Distinct (%)92.9%
Missing754
Missing (%)7.0%
Memory size1.0 MiB
2026-01-14T05:10:09.953741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length620
Median length229
Mean length41.763124
Min length6

Characters and Unicode

Total characters420847
Distinct characters147
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8874 ?
Unique (%)88.1%

Sample

1st rowDinçer, K.; Bozkus, Z.; Ranka, S.; Fox, G.
2nd rowJust, M.A.; Carpenter, P.A.; Varma, S.
3rd rowJohnson, A.A.; Tezduyar, T.
4th rowKalro, V.; Aliabadi, S.; Garrard, W.; Tezduyar, T.; Mittal, S.; Stein, K.
5th rowKashiyama, K.; Saitoh, K.; Behr, M.; Tezduyar, T.
ValueCountFrequency (%)
m2273
 
3.4%
a2055
 
3.1%
j1920
 
2.9%
s1881
 
2.8%
c1277
 
1.9%
d1127
 
1.7%
r1090
 
1.6%
y1024
 
1.5%
l1019
 
1.5%
k899
 
1.3%
Other values (16181)52743
78.4%
2026-01-14T05:10:10.468654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57229
 
13.6%
.43198
 
10.3%
,32894
 
7.8%
a25990
 
6.2%
;22884
 
5.4%
e17892
 
4.3%
n16896
 
4.0%
i15251
 
3.6%
r13961
 
3.3%
o13901
 
3.3%
Other values (137)160751
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)420847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
57229
 
13.6%
.43198
 
10.3%
,32894
 
7.8%
a25990
 
6.2%
;22884
 
5.4%
e17892
 
4.3%
n16896
 
4.0%
i15251
 
3.6%
r13961
 
3.3%
o13901
 
3.3%
Other values (137)160751
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)420847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
57229
 
13.6%
.43198
 
10.3%
,32894
 
7.8%
a25990
 
6.2%
;22884
 
5.4%
e17892
 
4.3%
n16896
 
4.0%
i15251
 
3.6%
r13961
 
3.3%
o13901
 
3.3%
Other values (137)160751
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)420847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
57229
 
13.6%
.43198
 
10.3%
,32894
 
7.8%
a25990
 
6.2%
;22884
 
5.4%
e17892
 
4.3%
n16896
 
4.0%
i15251
 
3.6%
r13961
 
3.3%
o13901
 
3.3%
Other values (137)160751
38.2%

authors_wos
Text

Missing 

Distinct3880
Distinct (%)92.6%
Missing6640
Missing (%)61.3%
Memory size615.1 KiB
2026-01-14T05:10:11.242175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length396
Median length140
Mean length39.709139
Min length5

Characters and Unicode

Total characters166421
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3663 ?
Unique (%)87.4%

Sample

1st rowBordewieck, M; Elson, M
2nd rowPan, ZX; Cui, Y; Leighton, JP; Cutumisu, M
3rd rowGough, P; Bown, O; Campbell, CR; Poronnik, P; Ross, PM
4th rowMagana, AJ; Coutinho, GS
5th rowMagana, AJ; de Jong, T
ValueCountFrequency (%)
m1011
 
3.5%
a940
 
3.2%
j709
 
2.4%
s692
 
2.4%
c608
 
2.1%
d505
 
1.7%
e435
 
1.5%
r424
 
1.4%
k407
 
1.4%
l388
 
1.3%
Other values (7385)23147
79.1%
2026-01-14T05:10:11.759034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25075
 
15.1%
,14498
 
8.7%
a10763
 
6.5%
;10335
 
6.2%
e8379
 
5.0%
n7264
 
4.4%
i6546
 
3.9%
o6328
 
3.8%
r6247
 
3.8%
l4301
 
2.6%
Other values (67)66685
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)166421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25075
 
15.1%
,14498
 
8.7%
a10763
 
6.5%
;10335
 
6.2%
e8379
 
5.0%
n7264
 
4.4%
i6546
 
3.9%
o6328
 
3.8%
r6247
 
3.8%
l4301
 
2.6%
Other values (67)66685
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)166421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25075
 
15.1%
,14498
 
8.7%
a10763
 
6.5%
;10335
 
6.2%
e8379
 
5.0%
n7264
 
4.4%
i6546
 
3.9%
o6328
 
3.8%
r6247
 
3.8%
l4301
 
2.6%
Other values (67)66685
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)166421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25075
 
15.1%
,14498
 
8.7%
a10763
 
6.5%
;10335
 
6.2%
e8379
 
5.0%
n7264
 
4.4%
i6546
 
3.9%
o6328
 
3.8%
r6247
 
3.8%
l4301
 
2.6%
Other values (67)66685
40.1%

author_full_names_wos
Text

Missing 

Distinct3897
Distinct (%)93.0%
Missing6640
Missing (%)61.3%
Memory size656.3 KiB
2026-01-14T05:10:12.101125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length568
Median length179
Mean length60.631114
Min length8

Characters and Unicode

Total characters254105
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3693 ?
Unique (%)88.1%

Sample

1st rowBordewieck, Martin; Elson, Malte
2nd rowPan, Zexuan; Cui, Ying; Leighton, Jacqueline P.; Cutumisu, Maria
3rd rowGough, Phillip; Bown, Oliver; Campbell, Craig R.; Poronnik, Philip; Ross, Pauline M.
4th rowMagana, Alejandra J.; Coutinho, Genisson Silva
5th rowMagana, Alejandra J.; de Jong, Ton
ValueCountFrequency (%)
m229
 
0.7%
a208
 
0.6%
wang161
 
0.5%
j153
 
0.5%
chen149
 
0.4%
maria144
 
0.4%
c133
 
0.4%
li132
 
0.4%
jose129
 
0.4%
zhang120
 
0.4%
Other values (11560)31763
95.3%
2026-01-14T05:10:12.609034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29130
 
11.5%
a24247
 
9.5%
e16655
 
6.6%
i16516
 
6.5%
n16447
 
6.5%
,14508
 
5.7%
r12137
 
4.8%
o11864
 
4.7%
;10335
 
4.1%
l8358
 
3.3%
Other values (50)93908
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)254105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29130
 
11.5%
a24247
 
9.5%
e16655
 
6.6%
i16516
 
6.5%
n16447
 
6.5%
,14508
 
5.7%
r12137
 
4.8%
o11864
 
4.7%
;10335
 
4.1%
l8358
 
3.3%
Other values (50)93908
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)254105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29130
 
11.5%
a24247
 
9.5%
e16655
 
6.6%
i16516
 
6.5%
n16447
 
6.5%
,14508
 
5.7%
r12137
 
4.8%
o11864
 
4.7%
;10335
 
4.1%
l8358
 
3.3%
Other values (50)93908
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)254105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29130
 
11.5%
a24247
 
9.5%
e16655
 
6.6%
i16516
 
6.5%
n16447
 
6.5%
,14508
 
5.7%
r12137
 
4.8%
o11864
 
4.7%
;10335
 
4.1%
l8358
 
3.3%
Other values (50)93908
37.0%

affiliations_scopus
Text

Missing 

Distinct8257
Distinct (%)82.9%
Missing872
Missing (%)8.1%
Memory size2.5 MiB
2026-01-14T05:10:13.828875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2979
Median length610
Mean length155.70509
Min length6

Characters and Unicode

Total characters1550667
Distinct characters166
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7396 ?
Unique (%)74.3%

Sample

1st rowNortheast Parallel Architectures Center, Syracuse University, Syracuse, NY, United States
2nd rowCenter for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, PA, United States; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United States
3rd rowArmy HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States
4th rowArmy HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States; Indian Institute of Technology Kanpur, Kanpur, UP, India; U.S. Army Natick RD and E Center, Natick, MA, United States
5th rowDepartment of Civil Engineering, Chuo University, Hachioji, Tokyo, Japan; University of Minnesota Twin Cities, Minneapolis, MN, United States
ValueCountFrequency (%)
of14845
 
7.6%
university9841
 
5.0%
united6752
 
3.5%
states5816
 
3.0%
department5189
 
2.7%
and4565
 
2.3%
de2667
 
1.4%
education2514
 
1.3%
china2344
 
1.2%
science2328
 
1.2%
Other values (9495)138073
70.8%
2026-01-14T05:10:15.388839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184972
 
11.9%
e124190
 
8.0%
n114070
 
7.4%
i111090
 
7.2%
a110084
 
7.1%
t93890
 
6.1%
o81846
 
5.3%
,68646
 
4.4%
r67966
 
4.4%
s53874
 
3.5%
Other values (156)540039
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1550667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
184972
 
11.9%
e124190
 
8.0%
n114070
 
7.4%
i111090
 
7.2%
a110084
 
7.1%
t93890
 
6.1%
o81846
 
5.3%
,68646
 
4.4%
r67966
 
4.4%
s53874
 
3.5%
Other values (156)540039
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1550667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
184972
 
11.9%
e124190
 
8.0%
n114070
 
7.4%
i111090
 
7.2%
a110084
 
7.1%
t93890
 
6.1%
o81846
 
5.3%
,68646
 
4.4%
r67966
 
4.4%
s53874
 
3.5%
Other values (156)540039
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1550667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
184972
 
11.9%
e124190
 
8.0%
n114070
 
7.4%
i111090
 
7.2%
a110084
 
7.1%
t93890
 
6.1%
o81846
 
5.3%
,68646
 
4.4%
r67966
 
4.4%
s53874
 
3.5%
Other values (156)540039
34.8%

affiliations_wos
Text

Missing 

Distinct2580
Distinct (%)64.8%
Missing6848
Missing (%)63.2%
Memory size669.9 KiB
2026-01-14T05:10:15.890864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length725
Median length266
Mean length68.173989
Min length5

Characters and Unicode

Total characters271537
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2014 ?
Unique (%)50.6%

Sample

1st rowRuhr University Bochum; Ruhr University Bochum
2nd rowUniversity of Alberta; University of Alberta
3rd rowUniversity of Sydney; University of New South Wales Sydney; University of Sydney; University of Sydney
4th rowPurdue University System; Purdue University; Purdue University System; Purdue University; Instituto Federal da Bahia (IFBA)
5th rowPurdue University System; Purdue University; University of Twente
ValueCountFrequency (%)
university6809
 
19.8%
of3864
 
11.2%
system1013
 
2.9%
de866
 
2.5%
state732
 
2.1%
universidad557
 
1.6%
technology500
 
1.5%
national465
 
1.3%
418
 
1.2%
normal390
 
1.1%
Other values (2089)18839
54.7%
2026-01-14T05:10:16.703097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30470
 
11.2%
i26367
 
9.7%
e21546
 
7.9%
n19635
 
7.2%
a16541
 
6.1%
t16513
 
6.1%
r14510
 
5.3%
o14251
 
5.2%
s13993
 
5.2%
y9654
 
3.6%
Other values (57)88057
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)271537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30470
 
11.2%
i26367
 
9.7%
e21546
 
7.9%
n19635
 
7.2%
a16541
 
6.1%
t16513
 
6.1%
r14510
 
5.3%
o14251
 
5.2%
s13993
 
5.2%
y9654
 
3.6%
Other values (57)88057
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)271537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30470
 
11.2%
i26367
 
9.7%
e21546
 
7.9%
n19635
 
7.2%
a16541
 
6.1%
t16513
 
6.1%
r14510
 
5.3%
o14251
 
5.2%
s13993
 
5.2%
y9654
 
3.6%
Other values (57)88057
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)271537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30470
 
11.2%
i26367
 
9.7%
e21546
 
7.9%
n19635
 
7.2%
a16541
 
6.1%
t16513
 
6.1%
r14510
 
5.3%
o14251
 
5.2%
s13993
 
5.2%
y9654
 
3.6%
Other values (57)88057
32.4%

addresses_wos
Text

Missing 

Distinct4120
Distinct (%)98.5%
Missing6648
Missing (%)61.4%
Memory size1.2 MiB
2026-01-14T05:10:17.076304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1245
Median length468
Mean length201.46761
Min length38

Characters and Unicode

Total characters842739
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4065 ?
Unique (%)97.2%

Sample

1st row[Bordewieck, Martin; Elson, Malte] Ruhr Univ Bochum, Fac Psychol, Bochum, Germany; [Bordewieck, Martin; Elson, Malte] Ruhr Univ Bochum, Horst Gortz Inst IT Secur, Bochum, Germany
2nd row[Pan, Zexuan; Cui, Ying; Leighton, Jacqueline P.; Cutumisu, Maria] Univ Alberta, Fac Educ, Ctr Res Appl Measurement & Evaluat, Dept Educ Psychol, Edmonton, AB, Canada; [Cutumisu, Maria] Univ Alberta, Fac Educ, Dept Educ Psychol, 6-102 Educ North, Edmonton, AB T6G 2G5, Canada
3rd row[Gough, Phillip] Univ Sydney, Affect Interact Lab, Sch Architecture Design & Planning, Camperdown, NSW, Australia; [Bown, Oliver] Univ New South Wales, Fac Art & Design, Kensington, NSW, Australia; [Campbell, Craig R.; Poronnik, Philip] Univ Sydney, Fac Med & Hlth, Sch Med Sci, FMH Media Lab,Educ Innovat, Camperdown, NSW, Australia; [Ross, Pauline M.] Univ Sydney, Fac Sci, Sch Life & Environm Sci, Camperdown, NSW 2006, Australia
4th row[Magana, Alejandra J.] Purdue Univ, Comp & Informat Technol & Engn Educ, W Lafayette, IN 47906 USA; [Coutinho, Genisson Silva] Purdue Univ, Engn Educ, W Lafayette, IN 47906 USA; [Coutinho, Genisson Silva] Inst Fed Educ Ciencia & Tecnol Bahia, Mech & Mat Technol Dept, Salvador, BA, Brazil
5th row[Magana, Alejandra J.] Purdue Univ, Dept Comp & Informat Technol, Knoy Hall Bldg,Room 231,401 N Grant St, W Lafayette, IN 47907 USA; [de Jong, Ton] Univ Twente, Fac Behav Management & Social Sci, Enschede, Netherlands
ValueCountFrequency (%)
univ7279
 
5.9%
educ3066
 
2.5%
2792
 
2.3%
dept2583
 
2.1%
usa2486
 
2.0%
sci1859
 
1.5%
technol1333
 
1.1%
sch1197
 
1.0%
china1158
 
0.9%
comp1137
 
0.9%
Other values (19011)97633
79.7%
2026-01-14T05:10:17.561401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
118340
 
14.0%
a62138
 
7.4%
n53955
 
6.4%
i48233
 
5.7%
e46325
 
5.5%
,43539
 
5.2%
o34533
 
4.1%
r30196
 
3.6%
l26567
 
3.2%
t25192
 
3.0%
Other values (64)353721
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)842739
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
118340
 
14.0%
a62138
 
7.4%
n53955
 
6.4%
i48233
 
5.7%
e46325
 
5.5%
,43539
 
5.2%
o34533
 
4.1%
r30196
 
3.6%
l26567
 
3.2%
t25192
 
3.0%
Other values (64)353721
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)842739
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
118340
 
14.0%
a62138
 
7.4%
n53955
 
6.4%
i48233
 
5.7%
e46325
 
5.5%
,43539
 
5.2%
o34533
 
4.1%
r30196
 
3.6%
l26567
 
3.2%
t25192
 
3.0%
Other values (64)353721
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)842739
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
118340
 
14.0%
a62138
 
7.4%
n53955
 
6.4%
i48233
 
5.7%
e46325
 
5.5%
,43539
 
5.2%
o34533
 
4.1%
r30196
 
3.6%
l26567
 
3.2%
t25192
 
3.0%
Other values (64)353721
42.0%
Distinct8181
Distinct (%)99.3%
Missing2589
Missing (%)23.9%
Memory size1.3 MiB
2026-01-14T05:10:17.863715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length930
Median length274
Mean length98.32577
Min length6

Characters and Unicode

Total characters810401
Distinct characters135
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8123 ?
Unique (%)98.6%

Sample

1st row4CAPS; Brain function; PET; T-MRI
2nd rowAutomobile; Complex geometries; Mesh generation; Parallel flow simulation
3rd row3D flow simulations; Parachutes; Parallel computations
4th rowImplicit space-time formulation; Parallel finite element method; Storm surge; Three-step explicit formulation; Tidal flow
5th rowCompressible flows; Missile aerodynamics; Parallel computing methods
ValueCountFrequency (%)
computational5251
 
6.3%
thinking5175
 
6.2%
education2817
 
3.4%
learning2307
 
2.8%
programming1539
 
1.8%
science1082
 
1.3%
design973
 
1.2%
computer850
 
1.0%
and717
 
0.9%
of602
 
0.7%
Other values (9148)61942
74.4%
2026-01-14T05:10:18.304590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
75003
 
9.3%
i70907
 
8.7%
n63449
 
7.8%
e59468
 
7.3%
t57522
 
7.1%
a55765
 
6.9%
o50958
 
6.3%
r35586
 
4.4%
;33956
 
4.2%
l32439
 
4.0%
Other values (125)275348
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)810401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
75003
 
9.3%
i70907
 
8.7%
n63449
 
7.8%
e59468
 
7.3%
t57522
 
7.1%
a55765
 
6.9%
o50958
 
6.3%
r35586
 
4.4%
;33956
 
4.2%
l32439
 
4.0%
Other values (125)275348
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)810401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
75003
 
9.3%
i70907
 
8.7%
n63449
 
7.8%
e59468
 
7.3%
t57522
 
7.1%
a55765
 
6.9%
o50958
 
6.3%
r35586
 
4.4%
;33956
 
4.2%
l32439
 
4.0%
Other values (125)275348
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)810401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
75003
 
9.3%
i70907
 
8.7%
n63449
 
7.8%
e59468
 
7.3%
t57522
 
7.1%
a55765
 
6.9%
o50958
 
6.3%
r35586
 
4.4%
;33956
 
4.2%
l32439
 
4.0%
Other values (125)275348
34.0%

index_keywords_scopus
Text

Missing 

Distinct6257
Distinct (%)99.9%
Missing4570
Missing (%)42.2%
Memory size2.1 MiB
2026-01-14T05:10:18.649547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2655
Median length690
Mean length280.32982
Min length7

Characters and Unicode

Total characters1755145
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6253 ?
Unique (%)99.9%

Sample

1st rowBandwidth; Calculations; Computational methods; Computer networks; Data communication systems; Distributed computer systems; Mathematical models; Parallel algorithms; Performance; Standards; Synchronization; Topology; Benchmarking; Communication latency; Communication start-up time; Computational processing rate; Control network; Diagnostic network; Gaussian elimination code; Global communication; Point to point communication; Vectorization; Parallel processing systems
2nd rowbrain function; cognition; computer model; computer simulation; conference paper; image processing; imaging system; nuclear magnetic resonance imaging; priority journal; Brain; Cognition; Humans; Magnetic Resonance Imaging; Models, Neurological; Neural Networks (Computer); Thinking
3rd rowAerodynamics; Automobiles; Computer simulation; Finite element method; Flow interactions; Navier Stokes equations; Parallel processing systems; Incompressible flow; Computational fluid dynamics; air flow; incompressible flow; Navier-Stokes equations; vehicles
4th rowComputational fluid dynamics; Computer simulation; Drag; Finite element method; Lift; Mathematical models; Navier Stokes equations; Newtonian flow; Parallel processing systems; Three dimensional computer graphics; Canopy inflation; Ram air parachutes; Parachutes; computer simulation; finite element method; parachutes
5th rowComputational fluid dynamics; Computer simulation; Finite element method; Parallel processing systems; Tides; Tidal flows; Unstructured grid formulations; Storms; computer simulation; finite element method; storms; tidal flows
ValueCountFrequency (%)
computational5667
 
3.1%
education4287
 
2.4%
learning4034
 
2.2%
computer3300
 
1.8%
thinkings3120
 
1.7%
programming2822
 
1.6%
students2640
 
1.5%
systems2371
 
1.3%
engineering1985
 
1.1%
computing1970
 
1.1%
Other values (11047)148514
82.2%
2026-01-14T05:10:19.150645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
174449
 
9.9%
e139615
 
8.0%
i137124
 
7.8%
n129507
 
7.4%
t117560
 
6.7%
a113948
 
6.5%
o107694
 
6.1%
;89057
 
5.1%
s85286
 
4.9%
r84748
 
4.8%
Other values (105)576157
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1755145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
174449
 
9.9%
e139615
 
8.0%
i137124
 
7.8%
n129507
 
7.4%
t117560
 
6.7%
a113948
 
6.5%
o107694
 
6.1%
;89057
 
5.1%
s85286
 
4.9%
r84748
 
4.8%
Other values (105)576157
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1755145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
174449
 
9.9%
e139615
 
8.0%
i137124
 
7.8%
n129507
 
7.4%
t117560
 
6.7%
a113948
 
6.5%
o107694
 
6.1%
;89057
 
5.1%
s85286
 
4.9%
r84748
 
4.8%
Other values (105)576157
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1755145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
174449
 
9.9%
e139615
 
8.0%
i137124
 
7.8%
n129507
 
7.4%
t117560
 
6.7%
a113948
 
6.5%
o107694
 
6.1%
;89057
 
5.1%
s85286
 
4.9%
r84748
 
4.8%
Other values (105)576157
32.8%

author_keywords_wos
Text

Missing 

Distinct3871
Distinct (%)99.4%
Missing6935
Missing (%)64.0%
Memory size780.2 KiB
2026-01-14T05:10:19.467230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length871
Median length215
Mean length98.48922
Min length3

Characters and Unicode

Total characters383714
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3849 ?
Unique (%)98.8%

Sample

1st rowcomputational thinking; networks; problem solving; troubleshooting; visual aids
2nd rowcognitive processes; computational thinking; think-aloud
3rd rowArduino; biomedical science education; creative code; data visualization; processing
4th rowcomputational thinking; engineering education; modeling and simulation; science and engineering workforce
5th rowgraduate; K-12; modeling; post-graduate; simulation
ValueCountFrequency (%)
thinking3068
 
7.9%
computational2983
 
7.7%
education1992
 
5.1%
learning1358
 
3.5%
programming1169
 
3.0%
science654
 
1.7%
computer549
 
1.4%
educational421
 
1.1%
design347
 
0.9%
robotics345
 
0.9%
Other values (3648)25895
66.8%
2026-01-14T05:10:19.939250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34885
 
9.1%
i33753
 
8.8%
n30795
 
8.0%
t27399
 
7.1%
e27146
 
7.1%
a26835
 
7.0%
o24543
 
6.4%
r16779
 
4.4%
c16048
 
4.2%
;15990
 
4.2%
Other values (68)129541
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)383714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
34885
 
9.1%
i33753
 
8.8%
n30795
 
8.0%
t27399
 
7.1%
e27146
 
7.1%
a26835
 
7.0%
o24543
 
6.4%
r16779
 
4.4%
c16048
 
4.2%
;15990
 
4.2%
Other values (68)129541
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)383714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
34885
 
9.1%
i33753
 
8.8%
n30795
 
8.0%
t27399
 
7.1%
e27146
 
7.1%
a26835
 
7.0%
o24543
 
6.4%
r16779
 
4.4%
c16048
 
4.2%
;15990
 
4.2%
Other values (68)129541
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)383714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
34885
 
9.1%
i33753
 
8.8%
n30795
 
8.0%
t27399
 
7.1%
e27146
 
7.1%
a26835
 
7.0%
o24543
 
6.4%
r16779
 
4.4%
c16048
 
4.2%
;15990
 
4.2%
Other values (68)129541
33.8%

keywords_plus_wos
Text

Missing 

Distinct2018
Distinct (%)79.4%
Missing8288
Missing (%)76.5%
Memory size499.5 KiB
2026-01-14T05:10:20.188745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length187
Median length142
Mean length47.801809
Min length2

Characters and Unicode

Total characters121560
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1900 ?
Unique (%)74.7%

Sample

1st rowVERBAL REPORTS; SKILLS; GAME; EDUCATION; TEACHERS; LEARN
2nd rowCOMPUTATIONAL THINKING; NARRATIVES; DESIGN; SKILLS; LEARN
3rd rowCURRICULUM; STUDENTS; SKILLS; MATHEMATICS; MOTIVATION; STANDARDS; SCIENCE
4th rowCOMPUTATIONAL THINKING; SCIENCE; SYSTEMS; DESIGN; TOOLS; K-12
5th rowCOMPUTATIONAL THINKING; AUTOMATED ASSESSMENT; ASSIGNMENTS
ValueCountFrequency (%)
thinking854
 
7.1%
computational760
 
6.4%
education443
 
3.7%
students337
 
2.8%
science323
 
2.7%
robotics313
 
2.6%
k-12294
 
2.5%
skills292
 
2.4%
design283
 
2.4%
mathematics206
 
1.7%
Other values (1415)7849
65.7%
2026-01-14T05:10:20.629892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E10770
 
8.9%
I10249
 
8.4%
T9781
 
8.0%
9411
 
7.7%
N8760
 
7.2%
A7596
 
6.2%
O7575
 
6.2%
;7322
 
6.0%
C6687
 
5.5%
S6591
 
5.4%
Other values (30)36818
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)121560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E10770
 
8.9%
I10249
 
8.4%
T9781
 
8.0%
9411
 
7.7%
N8760
 
7.2%
A7596
 
6.2%
O7575
 
6.2%
;7322
 
6.0%
C6687
 
5.5%
S6591
 
5.4%
Other values (30)36818
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)121560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E10770
 
8.9%
I10249
 
8.4%
T9781
 
8.0%
9411
 
7.7%
N8760
 
7.2%
A7596
 
6.2%
O7575
 
6.2%
;7322
 
6.0%
C6687
 
5.5%
S6591
 
5.4%
Other values (30)36818
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)121560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E10770
 
8.9%
I10249
 
8.4%
T9781
 
8.0%
9411
 
7.7%
N8760
 
7.2%
A7596
 
6.2%
O7575
 
6.2%
;7322
 
6.0%
C6687
 
5.5%
S6591
 
5.4%
Other values (30)36818
30.3%

abstract_scopus
Text

Missing 

Distinct9997
Distinct (%)98.9%
Missing724
Missing (%)6.7%
Memory size30.7 MiB
2026-01-14T05:10:21.026086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13301
Median length2092
Mean length1328.3593
Min length23

Characters and Unicode

Total characters13425727
Distinct characters426
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9990 ?
Unique (%)98.8%

Sample

1st rowThinking Machines' CM-5 machine is a distributed-memory, message-passing computer. In the paper we devise a performance benchmark for the base and vector units and the data communication networks of the CM-5 machine. We model the communication characteristics such as communication latency and bandwidths of point-to-point and global communication primitives. We show, on a simple Gaussian elimination code, that an accurate static performance estimation of parallel algorithms is possible by using those basic machine properties connected with computation, vectorization, communication and synchronization. Furthermore, we describe the embedding of meshes or hypercubes on the CM-5 fat-tree topology and illustrate the performance results of their basic communication primitives.
2nd rowThis article describes a computational modeling architecture, 4CAPS, which is consistent with key properties of cortical function and makes good contact with functional neuroimaging results. Like earlier cognitive models such as SOAR, ACT-R, 3CAPS, and EPIC, the proposed cognitive model is implemented in a computer simulation that predicts observable variables such as human response times and error patterns. In addition, the proposed 4CAPS model accounts for the functional decomposition of the cognitive system and predicts fMRI activation levels and their localization within specific cortical regions, by incorporating key properties of cortical function into the design of the modeling system.
3rd rowWe present our numerical methods for the solution of large-scale incompressible flow applications with complex geometries. These methods include a stabilized finite element formulation of the Navier-Stokes equations, implementation of this formulation on parallel architectures such as the Thinking Machines CM-5 and the CRAY T3D, and automatic 3D mesh generation techniques based on Delaunay-Voronoï methods for the discretization of complex domains. All three of these methods are required for the numerical simulation of most engineering applications involving fluid flow. We apply these methods to the simulation of airflow past an automobile and fluid-particle interactions. The simulation of airflow past an automobile is of very large scale with a high level of detail and yielded many interesting airflow patterns which help in understanding the aerodynamic characteristics of such vehicles. © 1997 by John Wiley & Sons, Ltd.
4th rowIn the near future, large ram-air parachutes are expected to provide the capability of delivering 21 ton pay loads from altitudes as high as 25,000 ft. In development and test and evaluation of these parachutes the size of the parachute needed and the deployment stages involved make high-performance computing (HPC) simulations a desirable alternative to costly airdrop tests. Although computational simulations based on realistic, 3D, time-dependent models will continue to be a major computational challenge, advanced finite element simulation techniques recently developed for this purpose and the execution of these techniques on HPC platforms are significant steps in the direction to meet this challenge. In this paper, two approaches for analysis of the inflation and gliding of ram-air parachutes are presented. In one of the approaches the point mass flight mechanics equations are solved with the time-varying drag and lift areas obtained from empirical data. This approach is limited to parachutes with similar configurations to those for which data are available. The other approach is 3D finite element computations based on the Navier-Stokes equations governing the airflow around the parachute canopy and Newton's law of motion governing the 3D dynamics of the canopy, with the forces acting on the canopy calculated from the simulated flow field. At the earlier stages of canopy inflation the parachute is modelled as an expanding box, whereas at the later stages, as it expands, the box transforms to a parafoil and glides. These finite element computations are carried out on the massively parallel supercomputers CRAY T3D and Thinking Machines CM-5, typically with millions of coupled, non-linear finite element equations solved simultaneously at every time step or pseudo-time step of the simulation. © 1997 by John Wiley & Sons, Ltd.
5th rowMassively parallel finite element methods for large-scale computation of storm surges and tidal flows are discussed here. The finite element computations, carried out using unstructured grids, are based on a three-step explicit formulation and on an implicit space-time formulation. Parallel implementations of these unstructured grid-based formulations are carried out on the Fujitsu Highly Parallel Computer AP1000 and on the Thinking Machines CM-5. Simulations of the storm surge accompanying the Ise-Bay typhoon in 1959 and of the tidal flow in Tokyo Bay serve as numerical examples. The impact of parallelization on this type of simulation is also investigated. The present methods are shown to be useful and powerful tools for the analysis of storm surges and tidal flows. © 1997 by John Wiley & Sons, Ltd.
ValueCountFrequency (%)
the108742
 
5.7%
and75116
 
3.9%
of72726
 
3.8%
to49923
 
2.6%
in47752
 
2.5%
a36450
 
1.9%
for20305
 
1.1%
that19229
 
1.0%
is18454
 
1.0%
this18326
 
1.0%
Other values (51028)1456269
75.7%
2026-01-14T05:10:21.611112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1912617
14.2%
e1280469
 
9.5%
t983849
 
7.3%
i928495
 
6.9%
n873567
 
6.5%
a853662
 
6.4%
o794139
 
5.9%
s736166
 
5.5%
r653733
 
4.9%
c463412
 
3.5%
Other values (416)3945618
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)13425727
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1912617
14.2%
e1280469
 
9.5%
t983849
 
7.3%
i928495
 
6.9%
n873567
 
6.5%
a853662
 
6.4%
o794139
 
5.9%
s736166
 
5.5%
r653733
 
4.9%
c463412
 
3.5%
Other values (416)3945618
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13425727
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1912617
14.2%
e1280469
 
9.5%
t983849
 
7.3%
i928495
 
6.9%
n873567
 
6.5%
a853662
 
6.4%
o794139
 
5.9%
s736166
 
5.5%
r653733
 
4.9%
c463412
 
3.5%
Other values (416)3945618
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13425727
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1912617
14.2%
e1280469
 
9.5%
t983849
 
7.3%
i928495
 
6.9%
n873567
 
6.5%
a853662
 
6.4%
o794139
 
5.9%
s736166
 
5.5%
r653733
 
4.9%
c463412
 
3.5%
Other values (416)3945618
29.4%

abstract_wos
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Memory size5.5 MiB
2026-01-14T05:10:22.259357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

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Median length1736
Mean length1300.9599
Min length81

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Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4101 ?
Unique (%)99.8%

Sample

1st rowTroubleshooting is a particular problem-solving process comprising error detection, fault diagnosis, and system restoration. Since automation of systems has become increasingly complex and ubiquitous, troubleshooting skills are crucial to maintain safety and security in a variety of contexts. The planned study aims at examining troubleshooting strategies and how to induce them by means of simple visual aids and concise instructions. To this end, a computerized task consisting of network troubleshooting problems will be employed in an experimental study with repeated measures. Indicators of strategy use and performance will be tested for their relation to availability and differential use of visual aids, to cognitive styles that affect how individuals deal with challenges or system information, and to cognitive processes that are involved in metacognition and executive function. The planned research is expected to help gain insights into the cognitive determinants of troubleshooting, reverse engineering, and their links to computational thinking.
2nd rowThis systematic review examines 35 empirical studies featuring the use of think-aloud interviews in computational thinking (CT) research. Findings show that think-aloud interviews (1) are typically conducted in Computer Science classrooms and with K-12 students; (2) are usually combined with other exploratory CT assessment tools; (3) have the potential to benefit learners with special needs and identify the competency gaps through involving diverse participants; (4) are conducted in the absence of cognitive models and standard procedures; and (5) display insufficient definitional and methodological rigor. Theoretically, this review presents a systematic assessment about the application of think-aloud interviews in CT studies and identifies the limitations in existing CT-related think-aloud studies. Practically, this review serves as a reference for studying the cognitive processes during CT problem-solving and provides suggestions for CT researchers who intend to incorporate think-aloud interviews in their studies.
3rd rowBiomedical science students need to learn to code. Graduates face a future where they will be better prepared for research higher degrees and the workforce if they can code. Embedding coding in a biomedical curriculum comes with challenges. First, biomedical science students often experience anxiety learning quantitative and computational thinking skills and second biomedical faculty often lack expertise required to teach coding. In this study, we describe a creative coding approach to building coding skills in students using the packages of Processing and Arduino. Biomedical science students were taught by an interdisciplinary faculty team from Medicine and Health, Science and Architecture, Design and Planning. We describe quantitative and qualitative responses of students to this approach. Cluster analysis revealed a diversity of student responses, with a large majority of students who supported creative coding in the curriculum, a smaller but vocal cluster, who did not support creative coding because either the exercises were not sufficiently challenging or were too challenging and believed coding should not be in a Biomedical Science curriculum. We describe how two creative coding platforms, Processing and Arduino, embedded and used to visualize human physiological data, and provide responses to students, including those minority of students, who are opposed to coding in the curriculum This study found a variety of students responses in a final year capstone course of an undergraduate Biomedical Science degree where future pathways for students are either in research higher degrees or to the workforce with a future which will be increasingly data driven.
4th rowComputational thinking has been recognized as a collection of understandings and skills required for new generations of students not only proficient at using tools, but also at creating them and understanding the implication of their capabilities and limitations. This study proposes the combination of modeling and simulation practices along with disciplinary learning as a way to synergistically integrate and take advantage of computational thinking in engineering education. This paper first proposes a framework that identifies different audiences of computing and related computational thinking practices at the intersection of computer science and engineering. Then, based on a survey with 37 experts from industry and academia, this paper also suggests a series of modeling and simulation practices, methods, and tools for such audiences. Finally, this paper also reports experts' identified challenges and opportunities for integrating modeling and simulation practices at the undergraduate level. (C) 2016 Wiley Periodicals, Inc.
5th rowMuch can be learned from the vast work on the use of computer simulations for inquiry learning for the integration of modeling and simulation practices in engineering education. This special issue presents six manuscripts that take steps toward evidence-based teaching and learning practices. These six studies present learning designs that align learning objectives, with evidence of the learning, and pedagogy. Here we highlight the main contributions from each paper individually, but also themes identified across all of them. These themes include (a) approaches for modeling-and-simulation-centric course design; (b) teaching practices and pedagogies for modeling and simulation implementation; and (c) evidence of learning with and about modeling and simulation practices. We conclude our introduction by highlighting desirable characteristics of studies that report on the effectiveness of modeling and simulation in engineering education, and with that we provide some recommendations for improving the scholarship of teaching and learning in this field.
ValueCountFrequency (%)
the42091
 
5.5%
and30892
 
4.1%
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3.5%
in20710
 
2.7%
to20458
 
2.7%
a14692
 
1.9%
students8732
 
1.1%
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1.1%
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1.0%
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1.0%
Other values (22029)573214
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2026-01-14T05:10:23.253280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
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14.2%
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9.7%
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7.5%
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6.9%
n353731
 
6.6%
a337460
 
6.3%
o313576
 
5.9%
s297583
 
5.6%
r261360
 
4.9%
c189019
 
3.5%
Other values (82)1551842
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5348246
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
756845
14.2%
e516582
 
9.7%
t399701
 
7.5%
i370547
 
6.9%
n353731
 
6.6%
a337460
 
6.3%
o313576
 
5.9%
s297583
 
5.6%
r261360
 
4.9%
c189019
 
3.5%
Other values (82)1551842
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5348246
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
756845
14.2%
e516582
 
9.7%
t399701
 
7.5%
i370547
 
6.9%
n353731
 
6.6%
a337460
 
6.3%
o313576
 
5.9%
s297583
 
5.6%
r261360
 
4.9%
c189019
 
3.5%
Other values (82)1551842
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5348246
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
756845
14.2%
e516582
 
9.7%
t399701
 
7.5%
i370547
 
6.9%
n353731
 
6.6%
a337460
 
6.3%
o313576
 
5.9%
s297583
 
5.6%
r261360
 
4.9%
c189019
 
3.5%
Other values (82)1551842
29.0%

publisher_scopus
Text

Missing 

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Missing (%)14.9%
Memory size800.9 KiB
2026-01-14T05:10:23.563829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

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Median length107
Mean length34.30987
Min length3

Characters and Unicode

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Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)5.2%

Sample

1st rowJohn Wiley and Sons Ltd
2nd rowJohn Wiley and Sons Ltd
3rd rowJohn Wiley and Sons Ltd
4th rowJohn Wiley and Sons Ltd
5th rowJohn Wiley and Sons Ltd
ValueCountFrequency (%)
and2674
 
6.7%
inc2138
 
5.4%
of1892
 
4.8%
springer1735
 
4.4%
institute1586
 
4.0%
association1424
 
3.6%
for1400
 
3.5%
engineers1200
 
3.0%
computing1161
 
2.9%
machinery1158
 
2.9%
Other values (1453)23334
58.8%
2026-01-14T05:10:24.044972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30482
 
9.6%
i26505
 
8.4%
n25571
 
8.1%
e24984
 
7.9%
r17878
 
5.7%
c17832
 
5.6%
t16783
 
5.3%
s16775
 
5.3%
a16284
 
5.1%
o15692
 
5.0%
Other values (70)107551
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)316337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
30482
 
9.6%
i26505
 
8.4%
n25571
 
8.1%
e24984
 
7.9%
r17878
 
5.7%
c17832
 
5.6%
t16783
 
5.3%
s16775
 
5.3%
a16284
 
5.1%
o15692
 
5.0%
Other values (70)107551
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)316337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
30482
 
9.6%
i26505
 
8.4%
n25571
 
8.1%
e24984
 
7.9%
r17878
 
5.7%
c17832
 
5.6%
t16783
 
5.3%
s16775
 
5.3%
a16284
 
5.1%
o15692
 
5.0%
Other values (70)107551
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)316337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
30482
 
9.6%
i26505
 
8.4%
n25571
 
8.1%
e24984
 
7.9%
r17878
 
5.7%
c17832
 
5.6%
t16783
 
5.3%
s16775
 
5.3%
a16284
 
5.1%
o15692
 
5.0%
Other values (70)107551
34.0%

publisher_wos
Text

Missing 

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Distinct (%)6.1%
Missing6639
Missing (%)61.3%
Memory size495.6 KiB
2026-01-14T05:10:24.390354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length74
Median length71
Mean length21.341842
Min length4

Characters and Unicode

Total characters89465
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)3.1%

Sample

1st rowWILEY
2nd rowWILEY
3rd rowWILEY
4th rowWILEY
5th rowWILEY
ValueCountFrequency (%)
springer896
 
7.4%
assoc867
 
7.2%
computing811
 
6.7%
machinery811
 
6.7%
ltd639
 
5.3%
506
 
4.2%
ieee431
 
3.6%
publishing367
 
3.0%
ag365
 
3.0%
francis338
 
2.8%
Other values (478)6076
50.2%
2026-01-14T05:10:24.910874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I8783
 
9.8%
E8296
 
9.3%
7915
 
8.8%
N7179
 
8.0%
S6163
 
6.9%
R6139
 
6.9%
A5907
 
6.6%
C5185
 
5.8%
O4522
 
5.1%
T4323
 
4.8%
Other values (46)25053
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)89465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I8783
 
9.8%
E8296
 
9.3%
7915
 
8.8%
N7179
 
8.0%
S6163
 
6.9%
R6139
 
6.9%
A5907
 
6.6%
C5185
 
5.8%
O4522
 
5.1%
T4323
 
4.8%
Other values (46)25053
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)89465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I8783
 
9.8%
E8296
 
9.3%
7915
 
8.8%
N7179
 
8.0%
S6163
 
6.9%
R6139
 
6.9%
A5907
 
6.6%
C5185
 
5.8%
O4522
 
5.1%
T4323
 
4.8%
Other values (46)25053
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)89465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I8783
 
9.8%
E8296
 
9.3%
7915
 
8.8%
N7179
 
8.0%
S6163
 
6.9%
R6139
 
6.9%
A5907
 
6.6%
C5185
 
5.8%
O4522
 
5.1%
T4323
 
4.8%
Other values (46)25053
28.0%

language_scopus
Categorical

High correlation  Imbalance  Missing 

Distinct16
Distinct (%)0.2%
Missing724
Missing (%)6.7%
Memory size592.6 KiB
English
9801 
Spanish
 
128
Chinese
 
83
Portuguese
 
45
Russian
 
19
Other values (11)
 
31

Length

Max length10
Median length7
Mean length7.0124666
Min length6

Characters and Unicode

Total characters70875
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English9801
90.5%
Spanish128
 
1.2%
Chinese83
 
0.8%
Portuguese45
 
0.4%
Russian19
 
0.2%
German5
 
< 0.1%
Polish4
 
< 0.1%
Italian4
 
< 0.1%
Croatian3
 
< 0.1%
French3
 
< 0.1%
Other values (6)12
 
0.1%
(Missing)724
 
6.7%

Length

2026-01-14T05:10:25.038870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english9801
97.0%
spanish128
 
1.3%
chinese83
 
0.8%
portuguese45
 
0.4%
russian19
 
0.2%
german5
 
< 0.1%
polish4
 
< 0.1%
italian4
 
< 0.1%
croatian3
 
< 0.1%
french3
 
< 0.1%
Other values (6)12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
s10105
14.3%
n10054
14.2%
i10049
14.2%
h10021
14.1%
g9847
13.9%
l9809
13.8%
E9801
13.8%
e273
 
0.4%
a180
 
0.3%
p131
 
0.2%
Other values (20)605
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)70875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s10105
14.3%
n10054
14.2%
i10049
14.2%
h10021
14.1%
g9847
13.9%
l9809
13.8%
E9801
13.8%
e273
 
0.4%
a180
 
0.3%
p131
 
0.2%
Other values (20)605
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)70875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s10105
14.3%
n10054
14.2%
i10049
14.2%
h10021
14.1%
g9847
13.9%
l9809
13.8%
E9801
13.8%
e273
 
0.4%
a180
 
0.3%
p131
 
0.2%
Other values (20)605
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)70875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s10105
14.3%
n10054
14.2%
i10049
14.2%
h10021
14.1%
g9847
13.9%
l9809
13.8%
E9801
13.8%
e273
 
0.4%
a180
 
0.3%
p131
 
0.2%
Other values (20)605
 
0.9%

language_wos
Categorical

High correlation  Imbalance  Missing 

Distinct11
Distinct (%)0.3%
Missing6639
Missing (%)61.3%
Memory size592.5 KiB
English
4064 
Spanish
 
86
Portuguese
 
25
French
 
4
German
 
3
Other values (6)
 
10

Length

Max length11
Median length7
Mean length7.0171756
Min length6

Characters and Unicode

Total characters29416
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English4064
37.5%
Spanish86
 
0.8%
Portuguese25
 
0.2%
French4
 
< 0.1%
German3
 
< 0.1%
Turkish3
 
< 0.1%
Russian2
 
< 0.1%
Korean2
 
< 0.1%
Unspecified1
 
< 0.1%
Chinese1
 
< 0.1%
(Missing)6639
61.3%

Length

2026-01-14T05:10:25.152892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english4064
96.9%
spanish86
 
2.1%
portuguese25
 
0.6%
french4
 
0.1%
german3
 
0.1%
turkish3
 
0.1%
russian2
 
< 0.1%
korean2
 
< 0.1%
unspecified1
 
< 0.1%
chinese1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s4184
14.2%
n4165
14.2%
i4160
14.1%
h4158
14.1%
g4089
13.9%
E4064
13.8%
l4064
13.8%
a95
 
0.3%
p87
 
0.3%
S86
 
0.3%
Other values (18)264
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)29416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s4184
14.2%
n4165
14.2%
i4160
14.1%
h4158
14.1%
g4089
13.9%
E4064
13.8%
l4064
13.8%
a95
 
0.3%
p87
 
0.3%
S86
 
0.3%
Other values (18)264
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s4184
14.2%
n4165
14.2%
i4160
14.1%
h4158
14.1%
g4089
13.9%
E4064
13.8%
l4064
13.8%
a95
 
0.3%
p87
 
0.3%
S86
 
0.3%
Other values (18)264
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s4184
14.2%
n4165
14.2%
i4160
14.1%
h4158
14.1%
g4089
13.9%
E4064
13.8%
l4064
13.8%
a95
 
0.3%
p87
 
0.3%
S86
 
0.3%
Other values (18)264
 
0.9%

Interactions

2026-01-14T05:09:57.359888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:53.613957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:54.317045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:54.979442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:55.972738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:56.687849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:57.478522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:53.723352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:54.431142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:55.095527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:56.092433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:56.799792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:57.589605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:53.834615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:54.544113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:55.535267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:56.222765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:56.903857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:57.702377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:53.944627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:54.649415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:55.641271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:56.340258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:57.005516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:57.821378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:54.060866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:54.764544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:55.755918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:56.457025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:57.119440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:57.939086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:54.195576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:54.869591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:55.864199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:56.568466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T05:09:57.235770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-14T05:10:25.244826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
cited_bydocument_typehas_scopushas_woslanguagelanguage_scopuslanguage_wosscopus_citationswos_citations_allwos_citations_corewos_reference_countyear
cited_by1.0000.0000.0000.0000.0000.0000.0001.0000.9650.9600.254-0.436
document_type0.0001.0000.6220.3280.0250.0130.0430.0350.0000.0000.4810.073
has_scopus0.0000.6221.0000.3360.1151.0000.1581.0000.0000.0000.0240.101
has_wos0.0000.3280.3361.0000.0850.0711.0000.0001.0001.0001.0000.293
language0.0000.0250.1150.0851.0001.0000.9370.0000.0000.0000.0000.000
language_scopus0.0000.0131.0000.0711.0001.0000.7940.0000.0000.0000.0000.000
language_wos0.0000.0430.1581.0000.9370.7941.0000.0000.0000.0000.0000.000
scopus_citations1.0000.0351.0000.0000.0000.0000.0001.0000.9600.9590.250-0.438
wos_citations_all0.9650.0000.0001.0000.0000.0000.0000.9601.0000.9840.278-0.442
wos_citations_core0.9600.0000.0001.0000.0000.0000.0000.9590.9841.0000.267-0.440
wos_reference_count0.2540.4810.0241.0000.0000.0000.0000.2500.2780.2671.0000.383
year-0.4360.0730.1010.2930.0000.0000.000-0.438-0.442-0.4400.3831.000

Missing values

2026-01-14T05:09:58.198190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-14T05:09:58.668317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-14T05:09:59.497982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

doititleyearjournaldocument_typepublisherlanguagecited_byscopus_citationswos_citations_corewos_citations_allscopus_reference_countwos_reference_countwos_categorieswos_research_areashas_scopushas_wosauthorsauthors_wosauthor_full_names_wosaffiliations_scopusaffiliations_wosaddresses_wosauthor_keywords_scopusindex_keywords_scopusauthor_keywords_woskeywords_plus_wosabstract_scopusabstract_wospublisher_scopuspublisher_woslanguage_scopuslanguage_wos
010.1002/(sici)1096-9128(199601)8:1<47::aid-cpe194>3.0.co;2-9Benchmarking the computation and communication performance of the CM-51996Concurrency Practice and ExperienceArticleJohn Wiley and Sons LtdEnglish2.02.0NaNNaNSolving Problems on Concurrent Processors, (1988); Hypercube Algorithms with Applications to Image Processing and Pattern Recognition, (1990); Bomans, Luc, Benchmarking the iPSC/2 hypercube multiprocessor, Concurrency Practice and Experience, 1, 1, pp. 3-18, (1989); Proceedings of the Frontiers of Massively Parallel Computation, (1992); Hockney, Roger W., Performance parameters and benchmarking of supercomputers, Parallel Computing, 17, 10-11, pp. 1111-1130, (1991); Kwan, Thomas T., Communication and computation performance of the CM-5, pp. 192-201, (1993); Leiserson, Charles E., Network architecture of the Connection Machine CM-5, pp. 272-285, (1992); Lin, Mengjou, Performance evaluation of the CM-5 interconnection network, pp. 189-198, (1993); Ponnusamy, Ravi, Experimental performance evaluation of the CM-5, Journal of Parallel and Distributed Computing, 19, 3, pp. 192-202, (1993); Bailey, David H., The nas parallel benchmarks, International Journal of High Performance Computing Applications, 5, 3, pp. 63-73, (1991)NaNNaNNaNTrueFalseDinçer, K.; Bozkus, Z.; Ranka, S.; Fox, G.NaNNaNNortheast Parallel Architectures Center, Syracuse University, Syracuse, NY, United StatesNaNNaNNaNBandwidth; Calculations; Computational methods; Computer networks; Data communication systems; Distributed computer systems; Mathematical models; Parallel algorithms; Performance; Standards; Synchronization; Topology; Benchmarking; Communication latency; Communication start-up time; Computational processing rate; Control network; Diagnostic network; Gaussian elimination code; Global communication; Point to point communication; Vectorization; Parallel processing systemsNaNNaNThinking Machines' CM-5 machine is a distributed-memory, message-passing computer. In the paper we devise a performance benchmark for the base and vector units and the data communication networks of the CM-5 machine. We model the communication characteristics such as communication latency and bandwidths of point-to-point and global communication primitives. We show, on a simple Gaussian elimination code, that an accurate static performance estimation of parallel algorithms is possible by using those basic machine properties connected with computation, vectorization, communication and synchronization. Furthermore, we describe the embedding of meshes or hypercubes on the CM-5 fat-tree topology and illustrate the performance results of their basic communication primitives.NaNJohn Wiley and Sons LtdNaNEnglishNaN
110.1002/(sici)1097-0193(1999)8:2/3<128::aid-hbm10>3.0.co;2-gComputational modeling of high-level cognition and brain function1999Human Brain MappingConference paperNaNEnglish73.073.0NaNNaNRules of the Mind, (1993); Awh, Edward, Dissociation of Storage and Rehearsal in Verbal Working Memory: Evidence from Positron Emission Tomography, Psychological Science, 7, 1, pp. 25-31, (1996); Agrammatism, (1985); Carpenter, Patricia Ann, Graded functional activation in the visuospatial system with the amount of task demand, Journal of Cognitive Neuroscience, 11, 1, pp. 9-24, (1999); Carpenter, Patricia Ann, What one intelligence test measures: A theoretical account of the processing in the Raven progressive matrices test, Psychological Review, 97, 3, pp. 404-431, (1990); Collins, Allan M., Retrieval time from semantic memory, Journal of Verbal Learning and Verbal Behavior, 8, 2, pp. 240-247, (1969); Rethinking Innateness A Connectionist Perspective on Development, (1996); Human Brain Function, (1997); Gabrieli, John D.E., The role of left prefrontal cortex in language and memory, Proceedings of the National Academy of Sciences of the United States of America, 95, 3, pp. 906-913, (1998); Grafman, Jordan Henry, Similarities and Distinctions among Current Models of Prefrontal Cortical Functions, Annals of the New York Academy of Sciences, 769, 1, pp. 337-368, (1995)NaNNaNNaNTrueFalseJust, M.A.; Carpenter, P.A.; Varma, S.NaNNaNCenter for Cognitive Brain Imaging, Carnegie Mellon University, Pittsburgh, PA, United States; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, United StatesNaNNaN4CAPS; Brain function; PET; T-MRIbrain function; cognition; computer model; computer simulation; conference paper; image processing; imaging system; nuclear magnetic resonance imaging; priority journal; Brain; Cognition; Humans; Magnetic Resonance Imaging; Models, Neurological; Neural Networks (Computer); ThinkingNaNNaNThis article describes a computational modeling architecture, 4CAPS, which is consistent with key properties of cortical function and makes good contact with functional neuroimaging results. Like earlier cognitive models such as SOAR, ACT-R, 3CAPS, and EPIC, the proposed cognitive model is implemented in a computer simulation that predicts observable variables such as human response times and error patterns. In addition, the proposed 4CAPS model accounts for the functional decomposition of the cognitive system and predicts fMRI activation levels and their localization within specific cortical regions, by incorporating key properties of cortical function into the design of the modeling system.NaNNaNNaNEnglishNaN
210.1002/(sici)1097-0363(199706)24:12<1321::aid-fld562>3.0.co;2-lParallel computation of incompressible flows with complex geometries1997International Journal for Numerical Methods in FluidsArticleJohn Wiley and Sons LtdEnglish100.0100.0NaNNaNTezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Añón, J. C R, Computation of incompressible flows with implicit finite element implementations on the Connection Machine, Computer Methods in Applied Mechanics and Engineering, 108, 1-2, pp. 99-118, (1993); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Computational Mechanics 95 Proc Int Conf on Computational Engineering Science, (1995); Pvm Parallel Virtual Machine, (1994); Añón, J. C R, An efficient communications strategy for finite element methods on the Connection Machine CM-5 system, Computer Methods in Applied Mechanics and Engineering, 113, 3-4, pp. 363-387, (1994); Mesh Generation and Update Strategies for Parallel Computation of Flow Problems with Moving Boundaries and Interfaces, (1995); Technical Report, (1995); Aliabadi, Shabrouz K., Parallel fluid dynamics computations in aerospace applications, International Journal for Numerical Methods in Fluids, 21, 10, pp. 783-805, (1995); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994)NaNNaNNaNTrueFalseJohnson, A.A.; Tezduyar, T.NaNNaNArmy HPC Research Center, College of Science and Engineering, Minneapolis, MN, United StatesNaNNaNAutomobile; Complex geometries; Mesh generation; Parallel flow simulationAerodynamics; Automobiles; Computer simulation; Finite element method; Flow interactions; Navier Stokes equations; Parallel processing systems; Incompressible flow; Computational fluid dynamics; air flow; incompressible flow; Navier-Stokes equations; vehiclesNaNNaNWe present our numerical methods for the solution of large-scale incompressible flow applications with complex geometries. These methods include a stabilized finite element formulation of the Navier-Stokes equations, implementation of this formulation on parallel architectures such as the Thinking Machines CM-5 and the CRAY T3D, and automatic 3D mesh generation techniques based on Delaunay-Voronoï methods for the discretization of complex domains. All three of these methods are required for the numerical simulation of most engineering applications involving fluid flow. We apply these methods to the simulation of airflow past an automobile and fluid-particle interactions. The simulation of airflow past an automobile is of very large scale with a high level of detail and yielded many interesting airflow patterns which help in understanding the aerodynamic characteristics of such vehicles. © 1997 by John Wiley & Sons, Ltd.NaNJohn Wiley and Sons LtdNaNEnglishNaN
310.1002/(sici)1097-0363(199706)24:12<1353::aid-fld564>3.0.co;2-6Parallel finite element simulation of large ram-air parachutes1997International Journal for Numerical Methods in FluidsArticleJohn Wiley and Sons LtdEnglish40.040.0NaNNaNAñón, J. C R, Development testing of large ram air inflated wings, (1993); Garrard, William L., Inflation analysis of ram air inflated gliding parachutes, pp. 186-198, (1995); Aliabadi, Shahrouz K., Parallel finite element computation of the dynamics of large ram air parachutes, pp. 278-293, (1995); Añón, J. C R, SEMI-EMPIRICAL THEORY TO PREDICT THE LOAD-TIME HISTORY OF AN INFLATING PARACHUTE., pp. 177-185, (1984); University of Minnesota Parachute Systems Technology Short Courses, (1994); AIAA Paper 91 0862, (1991); Tezduyar, Tayfun E., Parallel Finite-Element Computation of 3D Flows, Computer, 26, 10, pp. 27-36, (1993); Tezduyar, Tayfun E., Massively parallel finite element simulation of compressible and incompressible flows, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 157-177, (1994); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: I. The concept and the preliminary numerical tests, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 339-351, (1992); Tezduyar, Tayfun E., A new strategy for finite element computations involving moving boundaries and interfaces-The deforming-spatial-domain/space-time procedure: II. Computation of free-surface flows, two-liquid flows, and flows with drifting cylinders, Computer Methods in Applied Mechanics and Engineering, 94, 3, pp. 353-371, (1992)NaNNaNNaNTrueFalseKalro, V.; Aliabadi, S.; Garrard, W.; Tezduyar, T.; Mittal, S.; Stein, K.NaNNaNArmy HPC Research Center, College of Science and Engineering, Minneapolis, MN, United States; Indian Institute of Technology Kanpur, Kanpur, UP, India; U.S. Army Natick RD and E Center, Natick, MA, United StatesNaNNaN3D flow simulations; Parachutes; Parallel computationsComputational fluid dynamics; Computer simulation; Drag; Finite element method; Lift; Mathematical models; Navier Stokes equations; Newtonian flow; Parallel processing systems; Three dimensional computer graphics; Canopy inflation; Ram air parachutes; Parachutes; computer simulation; finite element method; parachutesNaNNaNIn the near future, large ram-air parachutes are expected to provide the capability of delivering 21 ton pay loads from altitudes as high as 25,000 ft. In development and test and evaluation of these parachutes the size of the parachute needed and the deployment stages involved make high-performance computing (HPC) simulations a desirable alternative to costly airdrop tests. Although computational simulations based on realistic, 3D, time-dependent models will continue to be a major computational challenge, advanced finite element simulation techniques recently developed for this purpose and the execution of these techniques on HPC platforms are significant steps in the direction to meet this challenge. In this paper, two approaches for analysis of the inflation and gliding of ram-air parachutes are presented. In one of the approaches the point mass flight mechanics equations are solved with the time-varying drag and lift areas obtained from empirical data. This approach is limited to parachutes with similar configurations to those for which data are available. The other approach is 3D finite element computations based on the Navier-Stokes equations governing the airflow around the parachute canopy and Newton's law of motion governing the 3D dynamics of the canopy, with the forces acting on the canopy calculated from the simulated flow field. At the earlier stages of canopy inflation the parachute is modelled as an expanding box, whereas at the later stages, as it expands, the box transforms to a parafoil and glides. These finite element computations are carried out on the massively parallel supercomputers CRAY T3D and Thinking Machines CM-5, typically with millions of coupled, non-linear finite element equations solved simultaneously at every time step or pseudo-time step of the simulation. © 1997 by John Wiley & Sons, Ltd.NaNJohn Wiley and Sons LtdNaNEnglishNaN
410.1002/(sici)1097-0363(199706)24:12<1371::aid-fld565>3.0.co;2-7Parallel finite element methods for large-scale computation of storm surges and tidal flows1997International Journal for Numerical Methods in FluidsArticleJohn Wiley and Sons LtdEnglish24.024.0NaNNaNAñón, J. C R, Tide and storm surge predictions using finite element model, Journal of Hydraulic Engineering, 118, 10, pp. 1373-1390, (1992); Añón, J. C R, Massively parallel finite element method for large scale computation of storm surge, 2, pp. 79-86, (1996); Añón, J. C R, Three‐step explicit finite element computation of shallow water flows on a massively parallel computer, International Journal for Numerical Methods in Fluids, 21, 10, pp. 885-900, (1995); Añón, J. C R, Selective lumping finite element method for shallow water flow, International Journal for Numerical Methods in Fluids, 2, 1, pp. 89-112, (1982); Tezduyar, Tayfun E., Computation of unsteady incompressible flows with the stabilized finite element methods: Space-time formulations, iterative strategies and massively parallel implementations, American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP, 246, pp. 7-24, (1992); Hughes, Thomas J.R., A multi-dimensioal upwind scheme with no crosswind diffusion., 34 )., pp. 19-35, (1979); Tezduyar, Tayfun E., FINITE ELEMENT FORMULATIONS FOR CONVECTION DOMINATED FLOWS WITH PARTICULAR EMPHASIS ON THE COMPRESSIBLE EULER EQUATIONS., (1983); Añón, J. C R, Finite element computation of compressible flows with the SUPG formulation, American Society of Mechanical Engineers, Fluids Engineering Division (Publication) FEDSM, 123, pp. 21-27, (1991); Añón, J. C R, SUPG finite element computation of compressible flows with the entropy and conservation variables formulations, Computer Methods in Applied Mechanics and Engineering, 104, 3, pp. 397-422, (1993); Aliabadi, Shabrouz K., SUPG finite element computation of viscous compressible flows based on the conservation and entropy variables formulations, Computational Mechanics, 11, 5-6, pp. 300-312, (1993)NaNNaNNaNTrueFalseKashiyama, K.; Saitoh, K.; Behr, M.; Tezduyar, T.NaNNaNDepartment of Civil Engineering, Chuo University, Hachioji, Tokyo, Japan; University of Minnesota Twin Cities, Minneapolis, MN, United StatesNaNNaNImplicit space-time formulation; Parallel finite element method; Storm surge; Three-step explicit formulation; Tidal flowComputational fluid dynamics; Computer simulation; Finite element method; Parallel processing systems; Tides; Tidal flows; Unstructured grid formulations; Storms; computer simulation; finite element method; storms; tidal flowsNaNNaNMassively parallel finite element methods for large-scale computation of storm surges and tidal flows are discussed here. The finite element computations, carried out using unstructured grids, are based on a three-step explicit formulation and on an implicit space-time formulation. Parallel implementations of these unstructured grid-based formulations are carried out on the Fujitsu Highly Parallel Computer AP1000 and on the Thinking Machines CM-5. Simulations of the storm surge accompanying the Ise-Bay typhoon in 1959 and of the tidal flow in Tokyo Bay serve as numerical examples. The impact of parallelization on this type of simulation is also investigated. The present methods are shown to be useful and powerful tools for the analysis of storm surges and tidal flows. © 1997 by John Wiley & Sons, Ltd.NaNJohn Wiley and Sons LtdNaNEnglishNaN
510.1002/(sici)1097-0363(199706)24:12<1417::aid-fld567>3.0.co;2-nParallel finite element computation of missile aerodynamics1997International Journal for Numerical Methods in FluidsArticleJohn Wiley and Sons LtdEnglish5.05.0NaNNaNParallel Finite Element Computations in Aerospace Applications, (1994); Advances in Numerical Simulation of Turbulent Flows, (1991); Añón, J. C R, The numerical computation of turbulent flows, Computer Methods in Applied Mechanics and Engineering, 3, 2, pp. 269-289, (1974); AIAA Paper 92 0670, (1983); A One Equation Turbulence Transport Model for High Reynolds Number Wall Bounded Flows, (1990); AIAA Paper 93 3099, (1993); Aliabadi, Shabrouz K., Parallel fluid dynamics computations in aerospace applications, International Journal for Numerical Methods in Fluids, 21, 10, pp. 783-805, (1995); Añón, J. C R, Implementation of implicit finite element methods for incompressible flows on the CM-5, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 95-111, (1994); Computational Mechanics 95 Proc Int Conf on Computational Engineering Science, (1995); Solving Large Scale Problems in Mechanics Parallel and Distributed Computer Applications, (1997)NaNNaNNaNTrueFalseSturek, W.B.; Ray, S.; Aliabadi, S.; Waters, C.; Tezduyar, T.NaNNaNU.S. Army Research Laboratory, Adelphi, MD, United States; College of Science and Engineering, Minneapolis, MN, United StatesNaNNaNCompressible flows; Missile aerodynamics; Parallel computing methodsAlgorithms; Compressible flow; Computational fluid dynamics; Finite element method; Mathematical models; Navier Stokes equations; Parallel processing systems; Reynolds number; Supersonic aerodynamics; Turbulence; Viscous flow; Thinking machines; Turbulence models; Missiles; aerodynamics; computational fluid dynamics; finite element method; missiles; Navier-Stokes equationsNaNNaNA flow simulation tool, developed by the authors at the Army HPC Research Center, for compressible flows governed by the Navier-Stokes equations is used to study missile aerodynamics at supersonic speeds, high angles of attack and for large Reynolds numbers. The goal of this study is the evaluation of this Navier-Stokes computational technique for the prediction of separated flow fields around high-length-to-diameter (L/D) bodies. In particular, this paper addresses two issues: (i) turbulence modelling with a finite element computational technique and (ii) efficient performance of the computational technique on two different multiprocessor mainframes, the Thinking Machines CM-5 and CRAY T3D. The paper first provides a discussion of the Navier-Stokes computational technique and the algorithm issues for achieving efficient performance on the CM-5 and T3D. Next, comparisons are shown between the computation and experiment for supersonic ramp flow to evaluate the suitability of the turbulence model. Following that, results of the computations for missile flow fields are shown for laminar and turbulent viscous effects. © 1997 by John Wiley & Sons, Ltd.NaNJohn Wiley and Sons LtdNaNEnglishNaN
610.1002/(sici)1097-0363(199706)24:12<1449::aid-fld569>3.0.co;2-8Parallel finite element calculation of flow in a three-dimensional lid-driven cavity using the CM-5 and T3D1997International Journal for Numerical Methods in FluidsArticleJohn Wiley and Sons LtdEnglish17.017.0NaNNaNAñón, J. C R, Theoretical Modeling of Czochralski Crystal Growth, MRS Bulletin, 13, 10, pp. 29-35, (1988); Science and Technology of Crystal Growth, (1995); Science and Technology of Crystal Growth, (1995); Añón, J. C R, Large-scale numerical analysis of materials processing systems: High-temperature crystal growth and molten glass flows, Computer Methods in Applied Mechanics and Engineering, 112, 1-4, pp. 69-89, (1994); Añón, J. C R, Massively parallel finite element computations of three-dimensional, time-dependent, incompressible flows in materials processing systems, Computer Methods in Applied Mechanics and Engineering, 119, 1-2, pp. 139-156, (1994); Añón, J. C R, Massively parallel finite element analysis of coupled, incompressible flows: A benchmark computation of baroclinic annulus waves, International Journal for Numerical Methods in Fluids, 21, 10, pp. 1007-1014, (1995); Añón, J. C R, Three-dimensional melt flows in Czochralski oxide growth: high-resolution, massively parallel, finite element computations, Journal of Crystal Growth, 152, 3, pp. 169-181, (1995); Añón, J. C R, On the effects of ampoule tilting during vertical Bridgman growth: Three-dimensional computations via a massively parallel, finite element method, Journal of Crystal Growth, 167, 1-2, pp. 292-304, (1996); Añón, J. C R, Analytical and numerical studies of the structure of steady separated flows, Journal of Fluid Mechanics, 24, 1, pp. 113-151, (1966); Añón, J. C R, High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method, Journal of Computational Physics, 48, 3, pp. 387-411, (1982)NaNNaNNaNTrueFalseYeckel, A.; Smith, J.W.; Derby, J.J.NaNNaNCollege of Science and Engineering, Minneapolis, MN, United States; College of Science and Engineering, Minneapolis, MN, United StatesNaNNaNFlow; Incompressible; Parallel finite element; Steady; Three-dimensionalBifurcation (mathematics); Cavitation; Convergence of numerical methods; Finite element method; Parallel processing systems; Reynolds number; Lid driven cavities; Computational fluid dynamics; cavities; finite element method; steady flowNaNNaNSteady flows in a three-dimensional lid-driven cavity at moderate Reynolds number are studied using various methods of parallel programming on the Cray T3D and Thinking Machines CM-5. These three-dimensional flows are compared with flows computed in a two-dimensional cavity. Solutions at Reynolds number up to 500 agree well with the experimental data of Aidun et al. (Phys. Fluids A, 3, 2081-2091 (1991)) for the location of separation of the secondary eddy at the downstream wall. Convergence of the three-dimensional problem using GMRES with diagonal preconditioning could not be obtained at Reynolds number greater than about 500. We speculate that the source of the difficulty is the loss of stability via pitchfork and Hopf bifurcations identified by Aidun et al. The relative performance of various methods of message passing on the Cray T3D is compared with the data-parallel mode of programming on the CM-5. No clear advantage between machines or message-passing methods is distinguished. © 1997 by John Wiley & Sons, Ltd.NaNJohn Wiley and Sons LtdNaNEnglishNaN
710.1002/(sici)1098-111x(199702)12:2<105::aid-int1>3.0.co;2-uA knowledge level analysis of taxonomic domains1997International Journal of Intelligent SystemsArticleJohn Wiley and Sons Inc.English5.05.0NaNNaNAñón, J. C R, The knowledge level, Artificial Intelligence, 18, 1, pp. 87-127, (1982); Clancey, William J., Heuristic classification, Artificial Intelligence, 27, 3, pp. 289-350, (1985); Añón, J. C R, Design problem solving. A task analysis, AI Magazine, 11, 4, pp. 59-71, (1990); Añón, J. C R, Components of expertise, AI Magazine, 11, 2, pp. 28-49, (1990); Second Generation Expert Systems, (1993); Añón, J. C R, KADS: a modelling approach to knowledge engineering, Knowledge Acquisition, 4, 1, pp. 5-53, (1992); Cc AI, (1993); Qualitative Reasoning and Decision Technologies, (1993); Sponges in Time and Space, (1994); Añón, J. C R, Managing linguistically expressed uncertainty in milord application to medical diagnosis, AI Communications, 1, 1, pp. 14-31, (1988)NaNNaNNaNTrueFalseDomingo, M.; Sierra, C.NaNNaNCSIC - Instituto de Investigación en Inteligencia Artificial (IIIA), Cerdanyola del Valles, Barcelona, SpainNaNNaNNaNArtificial intelligence; Computational methods; Computer software; Knowledge based systems; Knowledge representation; Programming theory; Domain models; Knowledge level theory; Knowledge engineeringNaNNaNThe Knowledge Level (KL) is an abstract level of description, prior to the symbol or software level, which aims at discovering the components of expertise without thinking of computational aspects. The KL analysis emphasizes the regularities in knowledge use for knowledge engineering. We consider the knowledge level analysis the AI counterpart of the specification of programs. Then, it must be possible to define formal ways of putting in relation the KL analysis with computational elements that implement it. The ultimate goal of the research presented in this article is to contribute in the filling of the gap between specification at the KL and implementation. To do so we propose (i) a particular interpretation of the three main concepts involved in the knowledge level theories, i.e., tasks, methods, and domain models, and (ii) a mapping between these notions and computational elements of Milord II, a shell developed at the IIIA Institute and used as the target programming environment of an example in biological identification. © 1997 John Wiley & Sons, Inc.NaNJohn Wiley and Sons Inc.NaNEnglishNaN
810.1002/(sici)1098-111x(199911)14:11<1071::aid-int1>3.0.co;2-jComputational verb systems: verb logic1999International Journal of Intelligent SystemsArticleJohn Wiley & Sons IncEnglish10.010.0NaNNaNBrain and Intelligence in Vertebrates, (1982); Three Pound Universe, (1986); Emperor S New Mind, (1989); English Verb Classes and Alternations A Preliminary Investigation, (1993); Technical Report Memorandum no Ucb Erl M97 66, (1997); Inform Sci, (1998); Internat J Gen Systems, (1998); Elements of English Grammar, (1901); Logic and Boolean Algebra, (1962); Logic and Algorithms, (1966)NaNNaNNaNTrueFalseYang, T.NaNNaNUniversity of California, Berkeley, Berkeley, CA, United StatesNaNNaNNaNBrain; Computational methods; Formal logic; Speech processing; Computational verb logic; Human natural languages; Verb logic; Verbs; Artificial intelligenceNaNNaNComputational verb systems are new platforms for artificial intelligence. They embed the dynamical knowledge expressed by dynamical experiences of human brains into machines by using pattern thinking. In this paper, the relationship defined by verbs in human natural languages is modeled by computational verb logic (verb logic for short). To unify different verbs with different contexts into comparable standard forms, the concepts of BE transformation and canonical BE transformation are given. The atomic and molecular verb sentences under canonical BE transformations are also defined for verb logic. The basic verb logic operations are given. Some examples are given to demonstrate the concepts of BE transformation and verb logic.NaNJohn Wiley & Sons IncNaNEnglishNaN
910.1002/(sici)1099-1743(200003/04)17:2<149::aid-sres290>3.0.co;2-qSystems research, genetic algorithms and information systems2000Systems Research and Behavioral ScienceArticleJohn Wiley and Sons LtdEnglish53.053.0NaNNaNManagement Science, (1971); AI Expert, (1994); Information and Economic Behavior, (1973); Back, Barbro, Neural networks and genetic algorithms for bankruptcy predictions, Expert Systems with Applications, 11, 4 SPEC. ISS., pp. 407-413, (1996); Knowledge Link how Firms Compete Through Strategic Alliances, (1991); Genetic Programming an Introduction, (1998); American Economic Review, (1966); Stern Information Systems Review, (1993); Chatterjee, Sangit, Genetic algorithms in statistics: Procedures and applications, Communications in Statistics Part B: Simulation and Computation, 26, 4, pp. 1617-1630, (1997); Cors Informs National Meeting at Montreal, (1998)NaNNaNNaNTrueFalseChaudhry, S.S.; Varano, M.W.; Xu, L.NaNNaNDepartment of Decision and Information Technologies, Villanova University, Villanova, PA, United States; Department of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China; Department of Decision and Information Technologies, Villanova University, Villanova, PA, United StatesNaNNaNComputational intelligence; Evolution; Genetic algorithms; Information systems; Intelligent systems; Soft computing; Systems science; Systems thinkingNaNNaNNaNDarwinian evolution and genetics have spawned a class of computational methods called evolutionary algorithms, and in particular, genetic algorithms. These evolutionary strategies provide new opportunities and challenges with ever-increasing applications in industry. In this paper, we propose that the proper context for a basic unifying theory of evolution for the emerging debate on the similarities and differences between biotic evolution and evolutionary algorithms is systems science. Recent changes in technology, coupled with developments in the field of artificial intelligence, promote the growth of enabling technologies, such as intelligent systems, in which we integrate genetic algorithms. Genetic algorithms are integrated with other artificial intelligence tools using a cooperating intelligent subsystem, which is integrated into the information systems of the organization. A portfolio of examples illustrating the evolving and expanding applications of genetic algorithms is included, as well as our computational experience with several commercially available genetic algorithm software. Copyright © 2000 John Wiley & Sons, Ltd.NaNJohn Wiley and Sons LtdNaNEnglishNaN
doititleyearjournaldocument_typepublisherlanguagecited_byscopus_citationswos_citations_corewos_citations_allscopus_reference_countwos_reference_countwos_categorieswos_research_areashas_scopushas_wosauthorsauthors_wosauthor_full_names_wosaffiliations_scopusaffiliations_wosaddresses_wosauthor_keywords_scopusindex_keywords_scopusauthor_keywords_woskeywords_plus_wosabstract_scopusabstract_wospublisher_scopuspublisher_woslanguage_scopuslanguage_wos
1082110.7659/j.issn.1005-6947.2021.02.006Analysis of key protein regulatory genes in differential expression profile of gallbladder cancer based on bioinformatics approaches; 基于生物信息学的胆囊癌差异表达谱中关键蛋白调控基因分析2021China Journal of General SurgeryArticleCentral South UniversityChinese3.03.0NaNNaNMiller, Kimberly D., Cancer treatment and survivorship statistics, 2016, Ca-A Cancer Journal for Clinicians, 66, 4, pp. 271-289, (2016); Hundal, Rajveer, Gallbladder cancer: Epidemiology and outcome, Clinical Epidemiology, 6, 1, pp. 99-109, (2014); Siegel, Rebecca L., Cancer statistics for Hispanics/Latinos, 2015, Ca-A Cancer Journal for Clinicians, 65, 6, pp. 457-480, (2015); Misra, S., Carcinoma of the gallbladder, The Lancet Oncology, 4, 3, pp. 167-176, (2003); Samuel, Sandeep, Clinicopathological characteristics and outcomes of rare histologic subtypes of gallbladder cancer over two decades: A population-based study, PLOS ONE, 13, 6, (2018); Roa, Juan C., Squamous cell and adenosquamous carcinomas of the gallbladder: Clinicopathological analysis of 34 cases identified in 606 carcinomas, Modern Pathology, 24, 8, pp. 1069-1078, (2011); Reid, Kaye M., Diagnosis and surgical management of gallbladder cancer: A review, Journal of Gastrointestinal Surgery, 11, 5, pp. 671-681, (2007); Henley, S. Jane, Gallbladder cancer incidence and mortality, United States 1999-2011, Cancer Epidemiology Biomarkers and Prevention, 24, 9, pp. 1319-1326, (2015); Kakaei, Farzad, Surgical treatment of gallbladder carcinoma: a critical review, Updates in Surgery, 67, 4, pp. 339-351, (2015); Lazcano-Ponce, Eduardo César, Epidemiology and molecular pathology of gallbladder cancer, Ca-A Cancer Journal for Clinicians, 51, 6, pp. 349-364, (2001)NaNNaNNaNTrueFalseHan, W.; Xin, W.; Su, S.; Wang, Q.NaNNaNDepartment of General Surgery, Air Force Medical University, Xi'an, Shaanxi, ChinaNaNNaNComputational Biology; Gallbladder Neoplasms; Gene Expression Profiling; Protein Interaction MapsNaNNaNNaNBackground and Aims: The underlying mechanism for the occurrence of gallbladder carcinoma (GBC) is still unclear at present. The available data at the genomic and transcriptomic levels provide the basic data source for investigation of the molecular biological mechanisms of GBC. Therefore, this study was conducted to to analyze the differentially expressed genes in and normal gallbladder tissues and key protein regulatory molecules in GBC by bioinformatics approaches, so as to explore the potential molecular biological mechanism of GBC. Methods: The differentially expressed genes were screened based on two GBC transcriptional datasets from GEO database, and the three GO functional annotations were performed on these genes. The STRING database was applied to construct a protein interaction network, and perform module mining in the investigation of key protein regulatory genes. Finally, the expression and predictive efficacy of the identified key protein regulatory genes were comprehensively evaluated. Results: A total of 140 repeatable differentially expressed genes (20 up-regulated genes and 120 down-regulated genes) in GBC were screened, which are mainly related to the forebrain development and positive regulation of neurogenesis, and participate in the composition of the postsynaptic membrane and transverse tubules. Meanwhile, the SFRP1, a key protein regulatory molecule, had a certain ability in predicting the occurrence of GBC. Conclusion: The information expressed by transcription spectrum of GBC obtained in this study can provide framework and thinking structure for studying the molecular mechanism of GBC. The key protein regulatory molecule SFRP1 probably plays a pivotal role in the occurrence and development of GBC. © Chinese Journal of General Surgery 2021.NaNCentral South UniversityNaNChineseNaN
1082210.7764/disena.21.article.7Generative Allegories of Oppression and Emancipation: Reflecting with Computational Social Models; Alegorías generativas de opresión y emancipación: Reflexionando con modelos sociales computacionales2022DisenaArticlePontificia Universidad Catolica de ChileEnglish0.00.0NaNNaNDesign Issues, (2004); Axelrod, Robert M., The dissemination of culture: A model with local convergence and global polarization, Journal of Conflict Resolution, 41, 2, pp. 203-226, (1997); Bertolotti, Francesco, Sensitivity to Initial Conditions in Agent-Based Models, Lecture Notes in Computer Science, 12520 LNAI, pp. 501-508, (2020); Social Agents Ecology Exchange and Evolution, (2002); On Anarchism, (2013); Costopoulos, André, How did sugarscape become a whole society model?, 7, pp. 259-269, (2015); Intuition Pumps and Other Tools for Thinking, (2013); Epstein, Joshua M., Agent-based computational models and generative social science, Complexity, 4, 5, pp. 41-60, (1999); Designs for the Pluriverse Radical Interdependence Autonomy and the Making of Worlds, (2018); Pedagogy of the Oppressed, (1970)NaNNaNNaNTrueFalseSosa, R.NaNNaNAuckland University of Technology, Auckland, AUK, New Zealand; Design & Architecture, Monash University, Melbourne, VIC, AustraliaNaNNaNChange agents; Creative research methods; Emergence; Multi-agent simulations; Thought experimentsNaNNaNNaNAThis paper presents a computational approach to growing artificial societies (agent-based simulations) as an explicit, accessible, and systematic tool to visualize and generate insights and new questions about Paulo Freire’s concepts of oppression and emancipation. These models do not make claims of validity or prediction, instead, their value is to structure our thinking and support our understanding. Here, I use computational social simulationas generative allegories to reflect upon the role of designers in participatory, co-design, and social design contexts. The paper shows how Freirean ideas can help reframe design as a pedagogical craft based on dialogue and collective inquiry. © 2022, Pontificia Universidad Catolica de Chile. All rights reserved.NaNPontificia Universidad Catolica de ChileNaNEnglishNaN
1082310.7765/9781526142290International law in Europe, 700–12002022NaNBookManchester University PressEnglish8.08.0NaNNaNundefined; undefined; undefined; undefined; undefined; Actes Concernant Les Vicomtes De Marseille Et Leurs Descendants, (1926); Acts of Welsh Rulers 1120 1283, (2005); undefined, (1991); undefined, (1969); Alcuin of York His Life and Letters, (1974)NaNNaNNaNTrueFalseBenham, J.NaNNaNNaNNaNNaNarbitration; expulsion; International law; legal justification; redress; treatiesComputational methods; Information systems; Information use; Kyoto Protocol; Law enforcement; Arbitration; Expulsion; Legal justification; Legal rules; Middle ages; Redress; International lawNaNNaN"It is the contention of this book that there was a notion of international law in the medieval period, and more specifically in the period 700 to 1200. It examines and analyses the ways and the extent to which such as system of rules was known and followed in the Middle Ages by exploring treaties as the main source of international law, and by following a known framework of evidencing it: that it was practised on a daily basis; that there was a reliance upon justification of action; that the majority of international legal rules were consistently obeyed; and finally, that it had the function to resolve disputed questions of fact and law. This monograph further considers problems such as enforcement, deterrence, authority, and jurisdiction, considering carefully how they can be observed in the medieval evidence, and challenging traditional ideas over their role and function in the history of international law. This monograph then, attempts to make a leap forward in thinking about how rulers, communities, and political entities conducted diplomacy and regulated their interactions with each other in a period before fully fledged nation states. © Jenny Benham 2022.NaNManchester University PressNaNEnglishNaN
1082410.7771/1541-5015.1754Tinkering with logo in an elementary mathematics methods course2018Interdisciplinary Journal of Problem-based LearningArticlePurdue University PressEnglish4.04.0NaNNaNCall for Manuscripts Special Issue Tinkering in Technology Rich Design Contexts, (2017); Barr, Valerie B., Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community?, ACM Inroads, 2, 1, pp. 48-54, (2011); Berland, Matthew W., Making, tinkering, and computational literacy, pp. 196-205, (2016); Berland, Matthew W., Using Learning Analytics to Understand the Learning Pathways of Novice Programmers, Journal of the Learning Sciences, 22, 4, pp. 564-599, (2013); Creative Computing, (2014); Proceedings of the 2012 Annual Meeting of the American Educational Research Association, (2012); Common Core State Standards for Mathematics, (2010); Focus in High School Mathematics Technology to Support Reasoning and Sense Making, (2011); Changing Minds Computers Learning and Literacy, (2000); Grover, Shuchi, Computational Thinking in K-12: A Review of the State of the Field, Educational Researcher, 42, 1, pp. 38-43, (2013)NaNNaNNaNTrueFalseValentine, K.D.NaNNaNWest Virginia University, Morgantown, WV, United StatesNaNNaNComputational literacy; Geometry; Mathematics education; Teacher educationNaNNaNNaNWith an increased push to integrate coding and computational literacy in K–12 learning environments, teacher educators will need to consider ways they might support preservice teachers (PSTs). This paper details a tinkering approach used to engage PSTs in thinking computationally as they worked with geometric concepts they will be expected to teach in K–5. Experiences programming in Logo to construct authentic artifacts in the form of two-dimensional geometric graphics not only supported PSTs’ understanding of core geometric and spatial concepts, but also helped them to make connections between mathematics and computational literacy. Artifacts and discourse are discussed as they relate to three core considerations: engaging learners to construct authentic artifacts, supporting a communitarian ethos, and supporting various types of rapid feedback. © 2018, Purdue University Press. All rights reserved.NaNPurdue University PressNaNEnglishNaN
1082510.7771/2157-9288.1296Making makers: Tracing stem identity in rural communities2021Journal of Pre-College Engineering Education ResearchArticlePurdue University PressEnglish10.010.0NaNNaNMaker Centered Learning and the Development of Self Preliminary Findings of the Agency by Design Project, (2015); Missing Makers how to Rebuild Americas Manufacturing Workforce; Crec Works Newsletter, (2008); Journal of Research in Rural Education, (2011); Barajas-López, Filiberto, Indigenous Making and Sharing: Claywork in an Indigenous STEAM Program, Equity and Excellence in Education, 51, 1, pp. 7-20, (2018); Makeology Makers as Learners, (2016); Barron, Brigid J.S., Predictors of creative computing participation and profiles of experience in two Silicon Valley middle schools, Computers and Education, 54, 1, pp. 178-189, (2010); Makeology Makers as Learners, (2016); Social Justice Education for Teachers Paulo Freire and the Possible Dream, (2008); Blikstein, Paulo, Children are not hackers: Building a culture of powerful ideas, deep learning, and equity in the maker movement, pp. 64-79, (2016)NaNNaNNaNTrueFalseNixon, J.; Stoiber, A.; Halverson, E.NaNNaNPBS Wisconsin Education, United States; University of Wisconsin-Madison, Madison, WI, United StatesNaNNaNComputational thinking; Engineering; Identity; Informal education; Makerspaces; Rural education; STEMNaNNaNNaNIn this article, we describe efforts to reduce barriers of entry to pre-college engineering in a rural community by training local teens to become maker-mentors and staff a mobile makerspace in their community. We bring a communities of practice frame to our inquiry, focusing on inbound and peripheral learning and identity trajectories as a mechanism for representing the maker-mentor experience. Through a longitudinal case study, we traced the individual trajectories of five maker-mentors over two years. We found a collection of interrelated factors present in those students who maintained inbound trajectories and those who remained on the periphery. Our research suggests that the maker-mentors who facilitated events in the community, taught younger community members about making, and co-facilitated with other maker-mentors were more likely to have inbound trajectories. We offer lessons learned from including a mentorship component in a pre-college maker program, an unusual design feature that afforded more opportunities to create inbound trajectories. A key affordance of the maker-mentor program was that it allowed teens to explore areas of making that were in line with their interests while still being a part of a larger community of practice. Understanding learning and identity trajectories will allow us to continually improve pre-college engineering programming and education opportunities that build on students’ funds of knowledge. © 2021, Purdue University Press. All rights reserved.NaNPurdue University PressNaNEnglishNaN
1082610.7821/naer.2021.7.640Investigating the Computational Thinking Ability of Young School Students Across Grade Levels in Two Different Types of Romanian Educational Institutions2021Journal of New Approaches in Educational ResearchArticleUniversidad de AlicanteEnglish7.07.09.010.0Ackerman, Phillip L., The locus of adult intelligence: Knowledge, abilities, and nonability traits, Psychology and Aging, 14, 2, pp. 314-330, (1999); Ahadi, Alireza, Performance and consistency in learning to program, ACM International Conference Proceeding Series, pp. 11-16, (2017); Aho, Alfred V., Computation and computational thinking, Computer Journal, 55, 7, pp. 833-835, (2012); Atmatzidou, Soumela, Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences, Robotics and Autonomous Systems, 75, pp. 661-670, (2016); Brackmann, Christian Puhlmann, Development of computational thinking skills through unplugged activities in primary school, ACM International Conference Proceeding Series, pp. 65-72, (2017); Brown, Neil C.C., Restart: The resurgence of computer science in UK schools, ACM Transactions on Computing Education, 14, 2, (2014); Byrne, Pat, The effect of student attributes on success in programming, pp. 49-52, (2001); Csta K 12 Computer Science Standards, (2017); del Olmo-Muñoz, Javier, Computational thinking through unplugged activities in early years of Primary Education, Computers and Education, 150, (2020); Denning, Peter J., The profession of IT: Beyond computational thinking, Communications of the ACM, 52, 6, pp. 28-30, (2009)40.0Education & Educational ResearchEducation & Educational ResearchTrueTrueKátai, Z.; Osztián, E.; Lörincz, B.Katai, Z; Osztian, E; Lorincz, BKatai, Zoltan; Osztian, Erika; Lorincz, BeataDepartment of Mathematics and Computer Science, Sapientia Erdélyi Magyar Tudományegyetem, Cluj Napoca, Cluj, RomaniaSapientia Hungarian University of Transylvania[Katai, Zoltan; Osztian, Erika; Lorincz, Beata] Sapientia Hungarian Univ Transylvania, Dept Math & Informat, Cluj Napoca, RomaniaALGORITHMS; COMPUTER-ASSISTED INSTRUCTION; CURRICULUM; EDUCATIONAL TESTING; GENDER ROLESNaNCOMPUTER-ASSISTED INSTRUCTION; ALGORITHMS; CURRICULUM; EDUCATIONAL TESTING; GENDER ROLESGENDEROver the last decade, continuous efforts have been made to bring computational thinking (CT) closer to K-12 education. These focused endeavors implicitly suggest that the current curricula do not sufficiently contribute to the development of learners’ CT. On the other hand, since CT is a combined skill with cross-disciplinary implications, one might conclude that even without an explicit focus on CS education, students’ CT might develop latently as they advance with the current curriculum. We have proposed to test whether differences exist in how 3rd-, 5th-, 7th- and 9th-grade learners from two Romanian educational institutions (girls vs. boys from Art vs. Theoretical school; 214 subjects with no prior experience in CT) relate to learning tasks that require a certain level of CT. The testing tool was inspired by the AlgoRythmics dance choreography illustration of the linear search algorithm and has the potential to reveal different levels of abstracting. Findings emphasize the need for a purposeful and coordinated CS infusion into K-9 education in order to accelerate students’ CT development. © 2021. The Author(s).Over the last decade, continuous efforts have been made to bring computational thinking (CT) closer to K-12 education. These focused endeavors implicitly suggest that the current curricula do not sufficiently contribute to the development of learners' CT. On the other hand, since CT is a combined skill with cross-disciplinary implications, one might conclude that even without an explicit focus on CS education, students' CT might develop latently as they advance with the current curriculum. We have proposed to test whether differences exist in how 3rd-, 5th-, 7th- and 9th-grade learners from two Romanian educational institutions (girls vs. boys from Art vs. Theoretical school; 214 subjects with no prior experience in CT) relate to learning tasks that require a certain level of CT. The testing tool was inspired by the AlgoRythmics dance choreography illustration of the linear search algorithm and has the potential to reveal different levels of abstracting. Findings emphasize the need for a purposeful and coordinated CS infusion into K-9 education in order to accelerate students' CT development.Universidad de AlicanteUNIV ALICANTE, GRUPO INVESTIGACION EDUTIC-ADEIEnglishEnglish
1082710.9779/pauefd.1438401An Overview of the Epistemological Link between Mathematical Thinking and Computational Thinking from Theory to Practice2025PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATIONArticleDERGIPARK AKADEnglish1.0NaN1.01.0NaN67.0Education & Educational ResearchEducation & Educational ResearchFalseTrueNaNAksoy, BD; Cantürk Günhan, B; Mumcu, FAksoy, Behiye Dincer; Canturk Gunhan, Berna; Mumcu, FilizNaNDokuz Eylul University; Dokuz Eylul University; Celal Bayar University[Aksoy, Behiye Dincer] Dokuz Eylul Univ, Egitim Bilimleri Enstitusu, Izmir, Turkiye; [Canturk Gunhan, Berna] Dokuz Eylul Univ, Izmir, Turkiye; [Mumcu, Filiz] Celal Bayar Univ, Yunusemre, TurkiyeNaNNaNMathematical thinking; Computational thinking; Mathematical modelingABSTRACTIONNaNMathematical thinking is critical to the maintenance of daily life and the development of science. This study examines the epistemological connection between mathematical and Computational Thinking (CT). Since CT involves the problem-solving process through thinking and computer science tools, it is thought to have an important relationship with mathematical thinking. In order to understand this relationship, mathematical modeling problems are addressed from a cognitive perspective in this study. Using a case study, a conceptual framework was developed by examining studies that attempt to integrate CT into mathematics education. In line with the framework, a professional development course was prepared and administered to a mathematics teacher. Subsequently, the relationship between the cognitive processes in the modeling process and the components of CT was examined. The study's findings revealed that (a) considering abstraction in the context of Piaget's abstraction theory is a more effective approach for understanding mathematical thinking, (b) more than one CT component can be identified at each stage of the modeling process, and no single component can be exclusively associated with a single stage, and (c) common and distinct thinking processes between mathematical thinking and CT were uncovered. These findings contribute to a more nuanced comprehension of the intricate interrelationships between mathematical thinking and cognitive flexibility.NaNDERGIPARK AKADNaNEnglish
1082810.9779/pauefd.696511The Relationship between Mathematics Teachers' Mind Types and Computational Thinking Skills2021PAMUKKALE UNIVERSITESI EGITIM FAKULTESI DERGISI-PAMUKKALE UNIVERSITY JOURNAL OF EDUCATIONArticleDERGIPARK AKADEnglish3.0NaN1.03.0NaN46.0Education & Educational ResearchEducation & Educational ResearchFalseTrueNaNHidiroglu, YÖ; Hidiroglu, ÇNHidiroglu, Yeliz Ozkan; Hidiroglu, Caglar NaciNaNMinistry of National Education - Turkey; Pamukkale University[Hidiroglu, Yeliz Ozkan] Republ Turkey Minist Natl Educ, Ankara, Turkey; [Hidiroglu, Caglar Naci] Pamukkale Univ, Math Educ, Denizli, TurkeyNaNNaNComputational thinking; the five minds for future; mathematics teacherFRAMEWORK; VALIDITYNaNThe aim of the study is to investigate the relationship between the mind types of mathematics teachers which will shape the future and their computational thinking skills. The study was designed according to quantitative-relational survey model. This study was carried out with 481 volunteer mathematics teachers determined according to the random sampling method. Computational Thinking Skills Scale and Mind Types Scale were used as data collection tools in the study. In the analysis of the data, descriptive statistics, correlation and regression analyses were benefited. According to the perceptions of the mathematics teachers, the level of their ethical mind and computational thinking skills are very high while their disciplined mind, synthesizing mind, creating mind, respectful mind and quinary mind levels are high. Also, according to the perceptions of mathematics teachers, there is a high level significant positive relationship between their quinary minds and computational thinking skills, and their quinary minds (both in sub-dimesnions and as a whole) are a significant predictor of their computational thinking.NaNDERGIPARK AKADNaNEnglish
1082910.9781/ijimai.2021.03.001Foundations for the design of a creative system based on the analysis of the main techniques that stimulate human creativity2021International Journal of Interactive Multimedia and Artificial IntelligenceArticleUniversidad Internacional de la RiojaEnglish2.02.0NaNNaNde Garrido, Luis, Agent-based modeling of collaborative creative processes with INGENIAS, AI Communications, 32, 3, pp. 223-233, (2019); Boden, Margaret A., Creativity and artificial intelligence, Artificial Intelligence, 103, 1-2, pp. 347-356, (1998); Artificial Intelligence, (1996); Jennings, Kyle E., Developing creativity: Artificial barriers in artificial intelligence, Minds and Machines, 20, 4, pp. 489-501, (2010); Intelligence Without Reason, (1991); Phi Delta Kappan, (1961); Jordanous, Anna, Four PPPPerspectives on computational creativity in theory and in practice, Connection Science, 28, 2, pp. 194-216, (2016); Lamb, Carolyn Elizabeth, Evaluating computational creativity: An interdisciplinary tutorial, ACM Computing Surveys, 51, 2, (2019); Wiggins, Geraint A., A preliminary framework for description, analysis and comparison of creative systems, Knowledge-Based Systems, 19, 7, pp. 449-458, (2006); Role of Creativity in the Management of Innovation, (2017)NaNNaNNaNTrueFalsede Garrido, L.; Gómez-Sanz, J.J.; Pavón, J.NaNNaNDepartment of Architecture, Universitat de València, Valencia, Valencia, Spain; Institute of Knowledge Technology, Universidad Complutense de Madrid, Madrid, Madrid, SpainNaNNaNArtificial Intelligence Creative System; Creativity; Lateral Thinking; Methods to Stimulate Human Creativity; Multi-agent SystemNaNNaNNaNThis work presents the design of a computational system with creative capacity, based on the synthesis of the main methods that stimulate human creativity. When analyzing each method, a set of characteristics that the computer system must have in order to emulate a creative capacity has been suggested. In this way, by integrating all the suggestions in a structured way, it is possible to design the general architecture and functioning strategy of a computer system that has the incremental creative capacity of well-known creative methods. This computational system is designed as a multi-agent system, made up of two groups of agents, the problem solving group and the creative group, the first one exploring and evaluating paths for suitable solutions, the second implementing creative methods to generate new paths that are provided to the first group. © 2021, Universidad Internacional de la Rioja. All rights reserved.NaNUniversidad Internacional de la RiojaNaNEnglishNaN
10830NaNCultivating Computational Thinking Skills via Educational Robotics Activities in a Blended Learning Environment2025CEUR Workshop ProceedingsConference paperCEUR-WSEnglish0.00.01.01.0Wing, Jeannette M., Computational thinking, Communications of the ACM, 49, 3, pp. 33-35, (2006); Developing Computational Thinking in Compulsory Education, (2016); Atmatzidou, Soumela, How Does the Degree of Guidance Support Students’ Metacognitive and Problem Solving Skills in Educational Robotics?, Journal of Science Education and Technology, 27, 1, pp. 70-85, (2018); Chiazzese, Giuseppe, Educational robotics in primary school: Measuring the development of computational thinking skills with the bebras tasks, Informatics, 6, 4, (2019); Chevalier, Morgane, Fostering computational thinking through educational robotics: a model for creative computational problem solving, International Journal of STEM Education, 7, 1, (2020); Ioannou, Andri, Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work, Education and Information Technologies, 23, 6, pp. 2531-2544, (2018); Alves Gomes, Andresa Shirley, Educational Robotics in Times of Pandemic: Challenges and Possibilities, (2020); Proceedings of the 2012 Annual Meeting of the American Educational Research Association, (2012); Román-González, Marcos, Combining Assessment Tools for a Comprehensive Evaluation of Computational Thinking Interventions, pp. 79-98, (2019); Chevalier, Morgane, The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school, Computers and Education, 180, (2022)34.0Computer Science, Interdisciplinary Applications; Education & Educational ResearchComputer Science; Education & Educational ResearchTrueTruePappa, N.Guan, X; Wei, GX; Jiang, B; Feng, XGuan, Xiu; Wei, Guoxia; Jiang, Bo; Feng, XiangUniversity of West Attica, Athens, Attica, GreeceEast China Normal University; Zhejiang University of Technology[Guan, Xiu; Jiang, Bo; Feng, Xiang] East China Normal Univ, Dept Educ Informat Technol, Shanghai, Peoples R China; [Wei, Guoxia] Zhejiang Univ Technol, Coll Educ, Hangzhou, Zhejiang, Peoples R ChinaBlended Learning; Computational Thinking; Educational Robotics; Robotics Simulators; Secondary Education1Adversarial machine learning; Educational robots; Federated learning; Robot learning; Blended learning; Blended learning environments; Computational thinkings; Educational levels; Educational robotics; Learning context; Learning outcome; Robotic simulator; Secondary education1; Thinking skills; Contrastive LearningComputational thinking; comparative analysis; meta-analysisLEARNING-PERFORMANCE; GAMEThere is a significant trend in the integration of Educational Robotics at all educational levels, and along with this, the promotion of Computational Thinking is one of the related learning outcomes of this integration. A t the s ame t ime, the t ransfer o f f ace-to-face learning to online or Blended Learning context due to the COVID-19 pandemic has led to the development of several technological tools, such as Educational Robotic simulators and online collaborating environments, to support this transfer. In this field, this PhD research aims to design a framework in which students collaborate in a Blended Learning context while solving Educational Robotic activities to cultivate Computational Thinking skills. © 2025 Copyright for this paper by its authors.With the advent of the era of intelligent education, the cultivation and development of computational thinking is the key in talent training. However, most of the existing researches focus on the design of computational thinking teaching methods and models on a small scale, and lack the test of the training effect. Moreover, these effects in existing research are also mixed and fuzzed, and there are even greater differences between the East and the West. Therefore, in order to be able to analyze the effects of computational thinking teaching in depth, meta-analysis can be used to extract the factors that influence the effects of computational thinking in the related research on computational thinking training in the primary school stage in the East and the West. Through the calculation of experimental effect size, the effects of different studies are merged, so as to present the true effect of computational thinking training. A total of 30 qualified literatures were filtered, and 278 effect values were extracted from them. Based on these, the difference in training effects between the East and the West can be calculated to further analyze the development differences of computational thinking in different regions and teaching methods, and then point out the direction for the improvement of computational thinking training methods and models between different regions. The main value of the research is promoting the innovative development of computational thinking training within the globe.CEUR-WSASIA PACIFIC SOC COMPUTERS IN EDUCATIONEnglishEnglish